Question: Generalized residual vector quantization technique for handling large-scale data volumes.

This paper presents an innovative method for aggregating local image descriptors into a compact representation suitable for efficient and accurate large-scale image search. The proposed technique offers a simplified yet effective alternative to the Fisher kernel representation, enabling the construction of a fixed-dimension vector from local descriptors. We further explore the optimization of dimensionality reduction and indexing algorithms in tandem, ensuring that vector comparison quality is maximally preserved. Our experimental results highlight the superiority of this approach over existing methods; it achieves search accuracy on par with the bag-of-features method while utilizing a mere 20 bytes of image representation. Notably, searching through a dataset of 10 million images can be accomplished in approximately 50 milliseconds, demonstrating the method's efficiency and scalability.

Digital collections of data continue to grow exponentially as the information age continues to infiltrate every aspect of society. These sources of data take many different forms such as unstructured text on the world wide web, sensor data, images, video, sound, results of scientific experiments and customer profiles for marketing. Clustering is an unsupervised learning approach that groups similar examples together without any human labeling of the data. Due to the very broad nature of this definition, there have been many different approaches to clustering explored in the scientific literature. This thesis addresses the computational efficiency of document clustering in an information retrieval setting. This includes compressed representations, efficient and scalable algorithms, and evaluation with a specific use case for increasing efficiency of a distributed and parallel search engine. Furthermore, it addresses the evaluation of document cluster quality for large scale collections containing millions to billions of documents where complete labeling of a collection is impractical. The cluster hypothesis from information retrieval is also tested using several different approaches throughout the thesis. This research introduces novel approaches to clustering algorithms, document representations, and clustering evaluation. The combination of document clustering algorithms and document representations outlined in this thesis are able to process large scale web search data sets. This has not previously been reported in the literature without resorting to sampling and producing a small number of clusters. Furthermore, the algorithms are able to do this on a single machine. However, they can also function in a distributed and parallel setting to further increase their scalability. This thesis introduces new clustering algorithms that can also be applied to problems outside the information retrieval domain. There are many large data sets being produced from advertising, social networks, videos, images, sound, satellites, sensor data, and a myriad of other sources. Therefore, I anticipate these approaches will become applicable to more domains, as larger data sets become available.

Analysing text-based data has become increasingly important due to the importance of text from sources such as social media, web contents, web searches. The growing volume of such data creates challenges for data analysis including efficient and scalable algorithm, effective computing platforms and energy efficiency. Compression is a standard method for reducing data size but current standard compression algorithms are destructive to the organisation of data contents. This work introduces Content-aware, Partial Compression (CaPC) for text using a dictionary-based approach. We simply use shorter codes to replace strings while maintaining the original data format and structure, so that the compressed contents can be directly consumed by analytic platforms. We evaluate our approach with a set of real-world datasets and several classical MapReduce jobs on Hadoop. We also provide a supplementary utility library for Hadoop, hence, existing MapReduce programs can be used directly on the compressed datasets with little or no modification. In evaluation, we demonstrate that CaPC works well with a wide variety of data analysis scenarios, experimental results show ~30% average data size reduction, and up to ~32% performance increase on some I/O intensive jobs on an in-house Hadoop cluster. While the gains may seem modest, the point is that these gains are 'for free' and act as supplementary to all other optimizations.

More and more real-time applications need to handle dynamic continuous queries over streaming data of high density. Conventional data and query indexing approaches generally do not apply for excessive costs in either maintenance or space. Aiming at these problems, this study first proposes a new indexing structure by fusing an adaptive cell and KDB-tree, namely CKDB-tree. A cell-tree indexing approach has been developed on the basis of the CKDB-tree that supports dynamic continuous queries. The approach significantly reduces the space costs and scales well with the increasing data size. Towards providing a scalable solution to filtering massive steaming data, this study has explored the feasibility to utilize the contemporary general-purpose computing on the graphics processing unit (GPGPU). The CKDB-tree-based approach has been extended to operate on both the CPU (host) and the GPU (device). The GPGPU-aided approach performs query indexing on the host while perform streaming data filtering on the device in a massively parallel manner. The two heterogeneous tasks execute in parallel and the latency of streaming data transfer between the host and the device is hidden. The experimental results indicate that (1) CKDB-tree can reduce the space cost comparing to the cell-based indexing structure by 60 percent on average, (2) the approach upon the CKDB-tree outperforms the traditional counterparts upon the KDB-tree by 66, 75 and 79 percent in average for uniform, skewed and hyper-skewed data in terms of update costs, and (3) the GPGPU-aided approach greatly improves the approach upon the CKDB-tree with the support of only a single Kepler GPU, and it provides real-time filtering of streaming data with 2.5M data tuples per second. The massively parallel computing technology exhibits great potentials in streaming data monitoring.

CH2118-8/85/0000-0387 $1.00 © 1985 IEEE 387 ABSTRACT. In this study a vector quantIzation (VQ) codebook was system. In the other, Shore and Burton 112] used word-based VQ used as an efficient means of characterizing the short-time spectral codebooks and reported good performance in speaker-trained isolatedfeatures of a speaker. A set of such codebooks were then used to word recognition experiments. Here, instead of using word-based VQ recognize the identity of an unknown speaker from his/her unlabelled spoken utterances based on a minimum distance (distortion) codebooks to characterize the phonetic contents of isolated words, we propose to use speaker-based VQ codebooks to characterize the classification rule. A series of speaker recognition experiments was variability of short-time acoustic features of speakers. performed using a 100-talker (50 male and 50 female) telephone recording database consisting of isolated digit utterances. For ten random but different isolated digits, over 98% speaker identification H. Speaker-based VQ Codebook Approach to Speaker accuracy was achieved. The effects, on performance, of different Characterization and Recognition system parameters such as codebook sizes, the number of test digits, phonetic richness of the text, and difference in recording sessions Were also studied in detail. A set of short-time raw feature vectors of a speaker can be used directly to represent the essential acoustical, phonological or physiological characteristics of that speaker if the training set includes sufficient variations. However such a direct representation is not practical when the number of training vectors is large. The memory requirements for storage and computational complexity in the recognition phase eventually become prohibitively high. Therefore an Automatic speaker recognition has long been an interesting and challenging problem to speech researchers [1-101. The problem, efficient way of compressing the training data had to be found. In order to compress the original data to a small set of representative depending upon the nature of the final task, can be classified into two points, we used a VQ codebook with a small number of codebook different categories: speaker verification and speaker identification. In entries. a speaker verification task, the recognizer is asked to verify an identity claim made by an unknown speaker and a decision to reject or accept the identity claim is made. In a speaker identification task the recognizer is asked to decide which out of a population of N speakers is best classified as the unknown speaker. The decision may include a choice of "none of the above" (i.e., a choice that the specific speaker is not in a given closed set of speakers). The input speech material used for speaker recognition can be either text-dependent (text-constrained) or text-independent (text-free). In the text-dependent mode the speaker is asked to utter a prescribed text. The utterance is then The speaker-based VQ codebook generation can be summarized as follows: Given a set of I training feature vectors, {a1,a2 a) characterizing the variability of a speaker, we want to find a partitioning of the feature vector space, {S1,S2 SM}, for that particular speaker where, 5, the whole feature space is represented as S S1 US2 U . . . US. Each partition, S, forms a convex, nonoverlapping region and every vector inside S is represented by the respo5'tis'1g ceatcoid. vector, b1, cf 5,. The p titioning is done in such a way that the average distortion

With the current rapid growth in multimedia technology, there is an imminent need for efficient techniclues to search and query large image databases. Because of their unique and peculiar needs, image databases cannot be treated iri a similar fashion to other types of digital libraries. The contextual dependencies present in images, and the complex nature of two-dimensional image data make the representation issues more difficult for image databases. An invariant representation of an image is still an open research issue. For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images. Distortions are very common in fingerprint images due to elasticity of the skin. In this paper, a method of indexincl large fingerprint image databases is presented. The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. The search engine has also been implemented on Splash 2-a field programmable gate array (FPGA)-based array processor to obtain near-,4SIC level speed of matching. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the

We present a novel sampling-based approximation technique for classical multidimensional scaling that yields an extremely fast layout algorithm suitable even for very large graphs. It produces layouts that compare favorably with other methods for drawing large graphs, and it is among the fastest methods available. In addition, our approach allows for progressive computation, i.e. a rough approximation of the layout can be produced even faster, and then be refined until satisfaction.

This paper presents an image super-resolution method that enhances spatial resolution of MRI images in the slice-select direction. The algorithm employs Tikhonov regularization, using a standard model of general imaging process and then reformulating the reconstruction as a regularized minimization problem. Our experimental result shows improvements in both signal-to-noise ratio and visual quality.

In most parallel DBSCAN algorithms, neighboring points are assigned to the same data partition for parallel processing to facilitate calculation of the density of the neighbors. This data partitioning scheme causes a few critical problems including load imbalance between data partitions, especially in a skewed data set. To remedy these problems, we propose a cell-based data partitioning scheme, pseudo random partitioning , that randomly distributes small cells rather than the points themselves. It achieves high load balance regardless of data skewness while retaining the data contiguity required for DBSCAN. In addition, we build and broadcast a highly compact summary of the entire data set, which we call a two-level cell dictionary , to supplement random partitions. Then, we develop a novel parallel DBSCAN algorithm, Random Partitioning-DBSCAN (shortly, RP-DBSCAN), that uses pseudo random partitioning together with a two-level cell dictionary. The algorithm simultaneously finds the local clusters to each data partition and then merges these local clusters to obtain global clustering. To validate the merit of our approach, we implement RP-DBSCAN on Spark and conduct extensive experiments using various real-world data sets on 12 Microsoft Azure machines (48 cores). In RP-DBSCAN, data partitioning and cluster merging are very light, and clustering on each split is not dragged out by a specific worker. Therefore, the performance results show that RP-DBSCAN significantly outperforms the state-of-the-art algorithms by up to 180 times.

A wide variety of distortion functions, such as squared Eucl idean distance, Mahalanobis distance, Itakura-Saito distance and relative entropy, have been use d for clustering. In this paper, we propose and analyze parametric hard and soft clustering algori thms based on a large class of distortion functions known as Bregman divergences. The proposed algor ithms unify centroid-based parametric clustering approaches, such as classical kmeans, the Linde-Buzo-Gray (LBG) algorithm and information-theoretic clustering, which arise by special hoices of the Bregman divergence. The algorithms maintain the simplicity and scalability of the c lassicalkmeans algorithm, while generalizing the method to a large class of clustering loss functi o s. This is achieved by first posing the hard clustering problem in terms of minimizing the loss i n Bregman information, a quantity motivated by rate distortion theory, and then deriving an it erative algorithm that monotonically decreases this loss. In addition, we show that there is a biject ion between regular exponential families and a large class of Bregman divergences, that we call regula r Bregman divergences. This result enables the development of an alternative interpretation o f a efficient EM scheme for learning mixtures of exponential family distributions, and leads to a si mple soft clustering algorithm for regular Bregman divergences. Finally, we discuss the connection be tween rate distortion theory and Bregman clustering and present an information theoretic analys is of Bregman clustering algorithms in terms of a trade-off between compression and loss in Bregman information.

Today's exponentially increasing data volumes and the high cost of storage make compression essential for the Big Data industry. Although research has concentrated on efficient compression, fast decompression is critical for analytics queries that repeatedly read compressed data. While decompression can be parallelized somewhat by assigning each data block to a different process, break-through speed-ups require exploiting the massive parallelism of modern multi-core processors and GPUs for data decompression within a block. We propose two new techniques to increase the degree of parallelism during decompression. The first technique exploits the massive parallelism of GPU and SIMD architectures. The second sacrifices some compression efficiency to eliminate data dependencies that limit parallelism during decompression. We evaluate these techniques on the decompressor of the DEFLATE scheme, called Inflate, which is based on LZ77 compression and Huffman encoding. We achieve a 2× speed-up in a head-to-head comparison with several multi core CPU-based libraries, while achieving a 17% energy saving with comparable compression ratios.


Question: An intelligent load management solution incorporating renewable energy integration for smart homes.

This paper delves into the role of Demand Response (DR), Distributed Generation (DG), and Distributed Energy Storage (DES) as critical components of the smart grid framework, collectively termed Distributed Energy Resources (DER). While DER is predominantly associated with the distribution level, its significance extends to the transmission system and wholesale markets, where it is often referred to as a "virtual power plant." In the realm of energy and ancillary service markets managed by Independent System Operators (ISOs) and Regional Transmission Organizations (RTOs), DER/DR can offer various market products such as energy, ancillary services, and capacity, contingent upon the specific market design and operational standards of the ISO/RTO.

The paper initiates by examining the key industry forces driving the smart grid evolution and the multifaceted nature of DER within this context. It then narrows its focus to DR, providing an overview of the current and emerging programs across different ISOs/RTOs and the markets they engage in. The discussion culminates with an exploration of the challenges and potential strategies for the effective deployment of DR under the smart grid and market paradigms.

Specifically addressing the topic of an intelligent load management system with renewable energy integration for smart homes, the paper underscores the critical role of demand response in optimizing energy consumption and facilitating the integration of renewable energy sources. It highlights the potential of smart grid technologies to enable more efficient and flexible management of household energy usage, aligning with the overarching goals of reducing carbon footprint and enhancing energy sustainability. The paper also touches upon the need for advanced algorithms and communication protocols to coordinate the dynamic interaction between energy generation, storage, and consumption, ensuring the seamless operation of smart homes within the smart grid ecosystem.

In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources and could effectively guide the participants to achieve individual welfare maximization in the system. To be specific, we develop a Lagrangian-based approach to transform the global optimization conducted by the power company into distributed optimization problems to obtain explicit energy consumption, supply, and price decisions for individual participants. Also, we show that these distributed problems derived from the global optimization by the power company are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate and formalize the vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks: Ex-ante attacks and Ex-post attacks, which are launched by the adversary before or after the decision-making process. We systematically analyze the welfare impacts of these attacks on the real-time pricing scheme. Through a combination of theoretical analysis and performance evaluation, our data shows that the real-time pricing scheme could effectively guide the participants to achieve welfare maximization, while cyber-attacks could significantly disrupt the results of real-time pricing decisions, imposing welfare reduction on the participants.

Traditional power grids are currently being transformed into smart grids (SGs). SGs feature multi-way communication among energy generation, transmission, distribution, and usage facilities. The reliable, efficient, and intelligent management of complex power systems requires integration of high-speed, reliable, and secure data information and communication technology into the SGs to monitor and regulate power generation and usage. Despite several challenges, such as trade-offs between wireless coverage and capacity as well as limited spectral resources in SGs, wireless communication is a promising SG communications technology. Cognitive radio networks (CRNs) in particular are highly promising for providing timely SG wireless communications by utilizing all available spectrum resources. We provide in this paper a comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies. We highlight potential applications of CR-based SG systems. We survey CR-based spectrum sensing approaches with their major classifications. We also provide a survey on CR-based routing and MAC protocols, and describe interference mitigation schemes. We furthermore present open issues and research challenges faced by CR-based SG networks along with future directions.

This paper presents the design and implementation of power converters for wind conversion systems. The power converter can not only transfer the power from a wind generator, but also improve the stability and safety of the system. The proposed system consists of a Permanent magnet synchronous generator (PMSG); a DC/DC boosts converter, a bi-directional DC/DC converter and a full-bridge DC/AC inverter. The wind generator is the main power source of the system, and the battery is used for energy storage and power compensation to recover the natural irregularity of the wind power. In this paper presents a new system configuration of the front-end rectifier stage for a hybrid wind or photo voltaic energy system. The configuration allows the two sources to supply the load separately or simultaneously, depending on the availability of energy sources. The inherent nature of this cuk-scpic fused converter, additional input filters are not necessary to filter out high frequency harmonic content is determinant for the generator life span, heating issue and efficiency. The fused multi-input rectifier stage also allows maximum power from the wind and sun. When it is available an adaptive MPPT algorithm will be used for photo voltaic (PV) system. Operational analysis of the proposed system, will discoursed in this paper simulation results are given to highlight the merit of the proposed circuit Index terms —Wind generator, PV and Fuel Cells, Bi-directional DC/DC converter, full-bridge DC/AC inverter, MPPT.

This paper focuses on the technology of the smart refrigerator connected through the Internet of Things (IoT) platform. It deals with the issue of food wastage and efficient food distribution through a network of connected refrigerators within a defined neighborhood. This paper defines the three-layer architecture: front-end layer, gateway layer and the back-end layer that can allow the implementation of the system. This low-cost network can be created through a microcontroller and wireless sensors at the front-end architecture. The process was discussed to determine: the stock item below a defined quantity or threshold limit, determining the product expiration date and eventually determining the purchase or exchange of food items across the network. It also includes the proof of concept technology by allowing connection of an ultrasonic sensor to an IoT platform that can allow the exchange of information on the positioning of the food items inside the refrigerator. This eventually can allow for a large-scale commercial implementation of the project to deal with the current issue of food distribution.

Electrical load forecasting plays an important role in the regular planning of power systems, in which load is influenced by several factors that must be analysed and identified prior to modelling in order to ensure better and instant load balancing between supply and demand. This paper proposes a two-stage approach for short-term electricity load forecasting. In the first stage, a set of day classes of load profiles are identified using K-means clustering algorithm alongside daily temperature estimation profiles. The proposed estimation method is particularly useful in case of lack of historical regular temperature data. While in the second stage, the stacked denoising autoencoders approach is used to build regression models able to forecast each day type independently. The obtained models are trained and evaluated using hourly electricity power data offered by Algeria’s National Electricity and Gas Company. Several models are investigated to substantiate the accuracy and effectiveness of the proposed approach.

Smart Grid is a modern electric power infrastructure that incorporates elements of traditional power systems and information and communication technology (ICT) with the aim to improve the reliability, efficiency and safety as crucial requirements of critical infrastructure systems. Relying on the ICT, Smart Grid exposes electric power systems to new security issues and vulnerabilities over the network. Therefore, the security is becoming an increasing concern at both physical and information levels. Access controls are one of the most important aspects of information security. Role-based access control (RBAC) model is widely used in complex systems that are characterized by many participants. However, the existing models are not suitable for critical infrastructure systems with specialized features, such as a high number of devices dispersed over the vast geographical regions in “an Internetlike distributed environment”. In this paper, access control mechanism optimized for Smart Grid systems is developed considering the regional division and concept of areas of responsibility (AOR). To this end, the standardized RBAC model will be extended to provide an efficient and consistent policy with greater level of granularity from the aspect of resource protection. The proposed RBACAOR model is formally described and then applied to the smart grid environment.

Smart homes can apply new Internet-Of-Things concepts along with RFID technologies for creating ubiquitous services. This paper introduces a novel read-out method for a hierarchical wireless master-slave RFID reader architecture of multi standard NFC (Near Field Communication) and UHF (Ultra High Frequency) technologies to build a smart home service system that benefits in terms of cost, energy consumption and complexity. Various smart home service use cases such as washing programs, cooking, shopping and elderly health care are described as examples that make use of this system.

Smart Grid (SG) achieves bidirectional energy and information flow between the energy user and the utility grid, allowing energy users not only to consume energy, but also to generate the energy and share with the utility grid or with other energy consumers. This type of energy user who consumes energy and who also can generate the energy is called the “prosumer”. The sustainability of the SG energy sharing process heavily depends on its participating prosumers, making prosumer participation and management schemes crucial within the energy sharing approaches. However in literature, there is very little attention on prosumer management schemes. The contribution of this article is twofold. First, this article introduces a novel concept of participating and managing the prosumers in the SG energy sharing process in the form of autonomous, intelligent goal-oriented virtual communities. Here, the community of prosumers can collectively increase the amount of power to be auctioned or bought offering higher bargaining power, thereby settling for a higher price per kilowatt in long-term. According to the literature, this research is the first of this type introducing a community approach for prosumer management. The initial step to build an effective prosumer-community is the identification of those prosumers who would be suitable to make efficient prosumer communities. This leads the necessity of identifying parameters that influence the energy sharing behaviours of prosumers. The second contribution of this article is that, this comprehensively analyzes the different parameters influencing the prosumer's energy sharing behaviours and thus presents multi-agent architecture for optimal prosumer-community formation.

This paper presents a novel control method for multimodule photovoltaic microinverter (MI). The proposed MI employs a two-stage topology with active-clamped current-fed push–pull converter cascaded with a full-bridge inverter. This system can operate in grid-connected mode to feed power to the grid with a programmable power factor. This system can also operate in line-interactive mode, i.e., share load power without feeding power to the grid. In the event of grid power failure, the MI can operate in a standalone mode to supply uninterruptible power to the load. This paper presents a multiloop control scheme with power programmable capability for achieving the above multiple functions. In addition, the proposed control scheme embedded a multimodule parallel capability that multiple MI modules can be paralleled to enlarge the capacity with autonomous control in all operation modes. Finally, three 250-W MI modules are adopted to demonstrate the effectiveness of the proposed control method in simulations as well as experiments.

This work proposes an integration of alternative energy sources having the different generating modules connected as current sources. These sources were configured to inject current into a common DC bus. This feature avoids current circulation among sources due to the voltage differences, reducing the stress in the individual generators. A battery bank is a system part, used as an energy accumulator for periods when there is primary energy depletion. The bus voltage is controlled by modulation of a secondary load in parallel with the main load. A DC-DC Boost converter connects each primary source to a common bus, except for the storage system that uses a DC-DC Buck-Boost converter. Finally, it is presented a mathematical analysis through simulated results to show the sources behavior and overall performance when turned into current sources.


Question: An intelligent load management solution incorporating renewable energy integration for smart home applications.

This paper delves into a comprehensive analysis of the role of communication and networking technologies in the development of Smart Grids, which are envisioned to integrate advanced capabilities into traditional electrical power grids to enhance their functionality and efficiency. The current scenario highlights the significance of advanced and swift communication devices and systems in mitigating issues like blackouts and voltage sags. To transition the existing power grid into a Smart Grid, the design and implementation of a novel communication infrastructure emerge as critical areas of investigation. Given that Smart Grid initiatives are relatively new and there has been a scarcity of comprehensive research and forward-looking requirements in this domain, this paper aims to fill this gap by conducting a systematic review of communication and networking in Smart Grids. The review encompasses various aspects such as the architectural framework for communication/networking, the diverse communication technologies that can be integrated into this framework, the consideration of quality of service (QoS), strategies for optimizing asset utilization, and control and management systems. The paper's objective is to provide a detailed overview of these elements, contributing to a better understanding of how communication and networking technologies can be harnessed to create more intelligent and efficient Smart Grids.

More than a decade of research in the field of thermal, motion, vibration and electromagnetic radiation energy harvesting has yielded increasing power output and smaller embodiments. Power management circuits for rectification and DC–DC conversion are becoming able to efficiently convert the power from these energy harvesters. This paper summarizes recent energy harvesting results and their power man-

Microgrid is a combination of distributed generators, storage systems, and controllable loads connected to low-voltage network that can operate either in grid-connected or in island mode. High penetration of power at distribution level creates such multiple microgrids. This paper proposes a two-level architecture for distributed-energy-resource management for multiple microgrids using multiagent systems. In order to match the buyers and sellers in the energy market, symmetrical assignment problem based on naíve auction algorithm is used. The developed mechanism allows the pool members such as generation agents, load agents, auction agents, grid agents, and storage agents to participate in market. Three different scenarios are identified based on the supply-demand mismatch among the participating microgrids. At the end of this paper, two case studies are presented with two and four interconnected microgrids participating in the market. Simulation results clearly indicate that the agent-based management is effective in resource management among multiple microgrids economically and profitably.

Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP)-based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear-programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures and, hence, works well on low-frequency data. Experimental results show that the proposed ALIP system performs better than conventional IP-based load disaggregation.

The improvement of the Quality of Life (QoL) and the enhancement of the Quality of Services (QoS) represent the main goal of every city evolutionary process. It is possible making cities smarter promoting innovative solutions by use of Information and Communication Technology (ICT) for collecting and analysing large amounts of data generated by several sources, such as sensor networks, wearable devices, and IoT devices spread among the city. The integration of different technologies and different IT systems, needed to build smart city applications and services, remains the most challenge to overcome. In the Smart City context, this paper intends to investigate the Smart Environment pillar, and in particular the aspect related to the implementation of Smart Energy Grid for citizens in the urban context. The innovative characteristic of the proposed solution consists of using the Blockchain technology to join the Grid, exchanging information, and buy/sell energy between the involved nodes (energy providers and private citizens), using the Blockchain granting ledger. Keywords— Information Technology, Smart City, Digital Revolution, Digital Innovation, Blockchain, Smart Energy Grid, Machine Learning.

In this study, a new method is proposed for the suppression of voltage amplitude fluctuations on the load terminals. Moreover, the voltage harmonics on the load terminals are effectively reduced below the defined limits in the IEEE-519 standard. In the studied topology, a twelve-switch three-phase voltage source converter is used for effective control on the zero-sequence components due to the unbalanced voltages or single phase voltage sags. In order to obtain a fast dynamic response, sliding mode control strategy is used to control the system. The existence condition of the sliding mode is presented. The performance of the closed-loop system is verified with cases of voltage sag and utility voltage harmonics. The overall system is explained in detail and verified by using MATLAB/Simulink.

In this paper, we present a smart service robotic system based on cloud computing services. The design and implementation of infrastructure, computation components and communication components are introduced. The proposed system can alleviate the complex computation and storage load of robots to cloud and provide various services to the robots. The computation components can dynamically allocate resources to the robots. The communication components allow easy access of the robots and provide flexible resource management. Furthermore, we modeled the task-scheduling problem and proposed a max-heaps algorithm. The simulation results demonstrate that the proposed algorithm minimized the overall task costs.

The Internet of Things envisions integration, coordination, communication, and collaboration of real-world objects in order to perform daily tasks in a more intelligent and efficient manner. To comprehend this vision, this article studies the design of a large-scale IoT system for smart grid application, which constitutes a large number of home users and has the requirement of fast response time. In particular, we focus on the messaging protocol of a universal IoT home gateway, where our cloud enabled system consists of a back-end server, a unified home gateway (UHG) at the end users, and a user interface for mobile devices. We discuss the features of such an IoT system to support a large-scale deployment with a UHG and real-time residential smart grid applications. Based on the requirements, we design an IoT system using XMPP and implemented in a testbed for energy management applications. To show the effectiveness of the designed testbed, we present some results using the proposed IoT architecture.

Currently, in the whole world, enormous electric energy is consumed by the street lights, which are automatically turn on when it becomes dark and automatically turn off when it becomes bright. This is the huge waste of energy in the whole world and should be changed. This paper discusses a smart street light system, whose concept is proposed by Fujii et al. The main aim of smart street light systems is that lights turn on when needed and light turn off when not needed. Moreover, the smart street light system in this paper behaves like usual street lights that turn on all night. The ideal behavior of the smart street light system is that no one finds turn-off of street lights at night. Whenever someone see street lights, they turn on and whenever no one see street lights, they turn off. The smart street light system consists of LED lights, brightness sensors, motion sensors and short-distance communication networks. The lights turn on before pedestrians and vehicles come and turn off or reduce brightness when there is no one. It will be difficult for pedestrians and drivers of vehicles to distinguish our smart street lights and the conventional street lights because our street lights all turn on before they come.

Reducing the energy consumption and associated greenhouse gas emissions of urban areas is paramount in research and practice, encompassing strategies to both reduce energy consumption and carbon intensity in both energy supply and demand. Most methods focus on one of these two approaches but few integrate decisions for supply and demand simultaneously. This paper presents a novel model that endogenously simulates energy supply and demand at a district scale on an hourly time scale. Demand is specified for a variety of building uses, and losses and municipal loads are calculated from the number of buildings in the district. Energy supply is modeled using technology-specific classes, allowing easy addition of specific equipment or types of energy generation. Standard interfaces allow expansion of the model to include new types of energy supply and demand. The model can be used for analysis of a single design alternative or optimization over a large design space, allowing exploration of various densities, mixes of uses, and energy supply technologies. An example optimization is provided for a community near San Francisco, California. This example uses 21 building types, 32 combined heat and power engines, and 16 chillers. The results demonstrate the ability to compare performance tradeoffs and optimize for three objectives: life cycle cost, annual carbon dioxide emissions, and overall system

This paper presents a novel control method for multimodule photovoltaic microinverter (MI). The proposed MI employs a two-stage topology with active-clamped current-fed push–pull converter cascaded with a full-bridge inverter. This system can operate in grid-connected mode to feed power to the grid with a programmable power factor. This system can also operate in line-interactive mode, i.e., share load power without feeding power to the grid. In the event of grid power failure, the MI can operate in a standalone mode to supply uninterruptible power to the load. This paper presents a multiloop control scheme with power programmable capability for achieving the above multiple functions. In addition, the proposed control scheme embedded a multimodule parallel capability that multiple MI modules can be paralleled to enlarge the capacity with autonomous control in all operation modes. Finally, three 250-W MI modules are adopted to demonstrate the effectiveness of the proposed control method in simulations as well as experiments.


Question: An intelligent load management solution incorporating renewable energy integration for smart home applications.

This paper presents a sophisticated power scheduling strategy for demand response within a home energy management system (HEMS), capitalizing on the advancements in smart grid technology. Home dwellers now possess the capability to autonomously plan their energy consumption, aiming to minimize expenses and mitigate the peak-to-average ratio (PAR) of electricity usage. Initially, a comprehensive framework for an energy management system (EMS) in a home area network (HAN) is delineated, grounded in the smart grid ecosystem. Subsequently, an efficacious scheduling methodology for domestic power utilization is introduced. The home gateway (HG) serves as the intermediary, receiving demand response (DR) data that includes real-time electricity tariffs. This information is relayed to the energy management controller (EMC), which, armed with the DR insights, formulates an optimal power scheduling blueprint. This blueprint is then disseminated to each household appliance by the HG, enabling automated operation in a financially savvy manner. The potential pitfall of adopting solely the real-time pricing (RTP) model is acknowledged; it could lead to a majority of appliances operating simultaneously during the lowest tariff periods, potentially straining the electrical grid due to an elevated PAR. To circumvent this issue, our research amalgamates RTP with the inclining block rate (IBR) model. By employing this dual-pricing strategy, our proposed scheduling technique not only curtails electricity costs but also diminishes the PAR, thereby bolstering the resilience of the entire electrical network. Recognizing the inherent nonlinearity of such optimization problems, a genetic algorithm is deployed to derive a solution, ensuring an effective and efficient power scheduling mechanism for demand response in HEMS.

In this paper, the cooperative control strategy of microsources and the energy storage system (ESS) during islanded operation is presented and evaluated by a simulation and experiment. The ESS handles the frequency and the voltage as a primary control. And then, the secondary control in microgrid management system returns the current power output of the ESS into zero. The test results show that the proposed cooperative control strategy can regulate the frequency and the voltage, and the secondary control action can contribute to improve the control capability.

The complexity and uncertainty in scheduling and operation of the power system are prominently increasing with the penetration of smart grid. An essential task for the effective operation of power systems is the power load forecasting. In this paper, a tandem data-driven method is studied in this research based on deep learning. A deep belief network (DBN) embedded with parametric Copula models is proposed to forecast the hourly load of a power grid. Data collected over a whole year from an urbanized area in Texas, United States is utilized. Forecasting hourly power load in four different seasons in a selected year is examined. Two forecasting scenarios, day-ahead and week-ahead forecasting are conducted using the proposed methods and compared with classical neural networks (NN), support vector regression machine (SVR), extreme learning machine (ELM), and classical deep belief networks (DBN). The accuracy of the forecasted power load is assessed by mean absolute percentage error (MAPE) and root mean square error (RMSE). Computational results confirm the effectiveness of the proposed semi-parametric data-driven method.

Homes are rich with information about people's energy consumption, medical health, and personal or family functions. In this paper, we present our experiences deploying large-scale residential sensing systems in over 20 homes. Deploying small-scale systems in homes can be deceptively easy, but in our deployments we encountered a phase transition in which deployment effort increases dramatically as residential deployments scale up in terms of 1) the number of nodes, 2) the length of time, and 3) the number of houses. In this paper, we distill our experiences down to a set of guidelines and design principles to help future deployments avoid the potential pitfalls of large-scale sensing in homes.

A solid-state transformer (SST) would be at least as efficient as a conventional version but would provide other benefits as well, particularly as renewable power sources become more widely used. Among its more notable strong points are on-demand reactive power support for the grid, better power quality, current limiting, management of distributed storage devices and a dc bus. Most of the nation's power grid currently operates one way - power flows from the utility to the consumer - and traditional transformers simply change voltage from one level to another. But smart transformers, based on power semiconductor switches, are more versatile. Not only can they change voltage levels, but also can effectively control the power flow in both directions. The development of a Solid State Transformer (SST) that incorporates a DC-DC multiport converter to integrate both photovoltaic (PV) power generation and battery energy storage is presented in this paper. The DC-DC stage is based on a quad active-bridge (QAB) converter which not only provides isolation for the load, but also for the PV and storage. The AC-DC stage is implemented with a pulse-width-modulated (PWM) single phase rectifier. A novel technique that complements the SISO controller by taking into account the cross coupling characteristics of the QAB converter is also presented herein. Cascaded SISO controllers are designed for the AC-DC stage. The QAB demanded power is calculated at the QAB controls and then fed into the rectifier controls in order to minimize the effect of the interaction between the two SST stages. The dynamic performance of the designed control loops based on the proposed control strategies are verified through extensive simulation of the SST average and switching models.

The Internet of Energy (IoE) provides an effective networking technology for distributed green energy, which allows the connection of energy anywhere at any time. As an important part of the IoE, electric vehicles (EVs), and charging pile management are of great significance to the development of the IoE industry. Previous work has mainly focused on network performance optimization for its management, and few studies have considered the security of the management between EVs and charging piles. Therefore, this paper proposes a decentralized security model based on the lightning network and smart contract in the blockchain ecosystem; this proposed model is called the lightning network and smart contract (LNSC). The overall model involves registration, scheduling, authentication, and charging phases. The new proposed security model can be easily integrated with current scheduling mechanisms to enhance the security of trading between EVs and charging piles. Experimental results according to a realistic infrastructure are presented in this paper. These experimental results demonstrate that our scheme can effectively enhance vehicle security. Different performances of LNSC-based scheduling strategies are also presented.

High-penetration levels of distributed photovoltaic (PV) generation on an electrical distribution circuit present several challenges and opportunities for distribution utilities. Rapidly varying irradiance conditions may cause voltage sags and swells that cannot be compensated by slowly responding utility equipment resulting in a degradation of power quality. Although not permitted under current standards for interconnection of distributed generation, fast-reacting, VAR-capable PV inverters may provide the necessary reactive power injection or consumption to maintain voltage regulation under difficult transient conditions. As side benefit, the control of reactive power injection at each PV inverter provides an opportunity and a new tool for distribution utilities to optimize the performance of distribution circuits, e.g., by minimizing thermal losses. We discuss and compare via simulation various design options for control systems to manage the reactive power generated by these inverters. An important design decision that weighs on the speed and quality of communication required is whether the control should be centralized or distributed (i.e., local). In general, we find that local control schemes are able to maintain voltage within acceptable bounds. We consider the benefits of choosing different local variables on which to control and how the control system can be continuously tuned between robust voltage control, suitable for daytime operation when circuit conditions can change rapidly, and loss minimization better suited for nighttime operation.

In this paper, the control strategy for a parallel-inverters-based microgrid is presented. The control strategy includes outer active and reactive power control loop based on frequency and voltage droop method to avoid critical communications among distributed generation units (DGs). The inner inverter control loop is employed to produce the required inverter output voltage provided by the outer power loop. In addition, two inverter control schemes are introduced and compared. This microgrid can operate at both grid-connected and islanded mode with proper power sharing capability between parallel DG systems. Moreover, smooth transition between operating modes is achieved without causing negative effect on the utility and critical loads. The performance of this control strategy is verified in simulation using Matlab/Simulink.

Nowadays, the remote Home Automation turns out to be more and more significant and appealing. It improves the value of our lives by automating various electrical appliances or instruments. This paper describes GSM (Global System Messaging) based secured device control system using App Inventor for Android mobile phones. App Inventor is a latest visual programming platform for developing mobile applications for Android-based smart phones. The Android Mobile Phone Platform becomes more and more popular among software developers, because of its powerful capabilities and open architecture. It is a fantastic platform for the real world interface control, as it offers an ample of resources and already incorporates a lot of sensors. No need to write programming codes to develop apps in the App Inventor, instead it provides visual design interface as the way the apps looks and use blocks of interlocking components to control the app’s behaviour. The App Inventor aims to make programming enjoyable and accessible to novices.

The energy industry has recently begun using smart meters to take fine-grained readings of energy usage. These smart meters enable flexible timeof-use billing, forecasting, and demand response, but they also raise serious user privacy concerns. We propose a novel technique for provably hiding sensitive power consumption information in the overall power consumption stream. Our technique relies on a rechargeable battery that is connected to the household’s power supply. This battery is used to modify the household’s power consumption by adding or subtracting noise (i.e., increasing or decreasing power consumption), in order to establish strong privacy guarantees in the sense of differential privacy. To achieve these privacy guarantees in realistic settings, we first investigate the influence of, and the interplay between, capacity and throughput bounds that batteries face in reality. We then propose an integrated method based on noise cascading that allows for recharging the battery on-the-fly so that differential privacy is retained, while adhering to capacity and throughput constraints, and while keeping the additional consumption of energy induced by our technique to a minimum.

This paper presents a flexible ac distribution system device for microgrid applications. The device aims to improve the power quality and reliability of the overall power distribution system that the microgrid is connected to. The control design employs a new model predictive control algorithm which allows faster computational time for large power systems by optimizing the steady-state and the transient control problems separately. Extended Kalman filters are also employed for frequency tracking and to extract the harmonic spectra of the grid voltage and the load currents in the microgrid. The design concept is verified through different test case scenarios to demonstrate the capability of the proposed device and the results obtained are discussed.


Question: An intelligent load management solution incorporating renewable energy integration for smart home applications.

This paper presents an innovative approach for optimal residential energy consumption scheduling in real-time electricity pricing environments, aiming to strike a balance between minimizing electricity costs and reducing the waiting time for appliance operation. Real-time pricing models offer the potential for economic and environmental gains over traditional flat rates by allowing consumers to adjust their consumption based on time-varying prices. However, challenges arise from users' unfamiliarity with how to react to fluctuating prices and the absence of efficient building automation systems. To address these issues, we propose a user-friendly, automated framework for energy consumption scheduling that utilizes linear programming to optimize energy use under real-time pricing tariffs combined with inclining block rates. The system is designed to require minimal user intervention. Furthermore, we emphasize the importance of price prediction in residential load control strategies, especially when utilities provide price information only one or two hours in advance. To this end, we apply a straightforward weighted average price prediction filter to hourly price data from the Illinois Power Company spanning January 2007 to December 2009. This process identifies the optimal coefficients for each day of the week to be used by the price predictor. Simulation outcomes demonstrate that the integration of our energy consumption scheduling design with the price prediction filter results in substantial reductions in user expenses and the peak-to-average load demand ratio across diverse load scenarios, benefiting both consumers and utility companies. This underscores the effectiveness of deploying our optimal energy consumption scheduling systems in smart homes with renewable energy integration.

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.

Climate changes and rainfall has been erratic over the past decade. Due to this in recent era, climate-smart methods called as smart agriculture is adopted by many Indian farmers. Smart agriculture is an automated and directed information technology implemented with the IOT (Internet of Things). IOT is developing rapidly and widely applied in all wireless environments. In this paper, sensor technology and wireless networks integration of IOT technology has been studied and reviewed based on the actual situation of agricultural system. A combined approach with internet and wireless communications, Remote Monitoring System (RMS) is proposed. Major objective is to collect real time data of agriculture production environment that provides easy access for agricultural facilities such as alerts through Short Massaging Service (SMS) and advices on weather pattern, crops etc.

Buildings are known to be the largest consumers of electricity in the United States, and often times the dominant energy consumer is the HVAC system. Despite this fact, in most buildings the HVAC system is run using primitive static control algorithms based on fixed work schedules causing wasted energy during periods of low occupancy. In this paper we present a novel control architecture that uses occupancy sensing to guide the operation of a building HVAC system. We show how we can enable aggressive duty-cycling of building HVAC systems — that is, turn them ON or OFF — to save energy while meeting building performance requirements using inexpensive sensing and control methods. We have deployed our occupancy sensor network across an entire floor of a university building and our data shows several periods of low occupancy with significant opportunities to save energy over normal HVAC schedules. Furthermore, by interfacing with the building Energy Management System (EMS) directly and using real-time occupancy data collected by our occupancy nodes, we measure electrical energy savings of 9.54% to 15.73% and thermal energy savings of 7.59% to 12.85% for the HVAC system by controlling just one floor of our four floor building.

A Non-Intrusive Load Monitoring (NILM) method, robust even in the presence of unlearned or unknown appliances (UUAs) is presented in this paper. In the absence of such UUAs, this NILM algorithm is capable of accurately identifying each of the turned-ON appliances as well as their energy levels. However, when there is an UUA or set of UUAs are turned-ON during a particular time window, proposed NILM method detects their presence. This enables the operator to detect presence of anomalies or unlearned appliances in a household. This quality increases the reliability of the NILM strategy and makes it more robust compared to existing NILM methods. The proposed Robust NILM strategy (RNILM) works accurately with a single active power measurement taken at a low sampling rate as low as one sample per second. Here first, a unique set of features for each appliance was extracted through decomposing their active power signal traces into uncorrelated subspace components (SCs) via a high-resolution implementation of the Karhunen-Loeve (KLE). Next, in the appliance identification stage, through considering power levels of the SCs, the number of possible appliance combinations were rapidly reduced. Finally, through a Maximum a Posteriori (MAP) estimation, the turned-ON appliance combination and/or the presence of UUA was determined. The proposed RNILM method was validated using real data from two public databases: Reference Energy Disaggregation Dataset (REDD) and Tracebase. The presented results demonstrate the capability of the proposed RNILM method to identify, the turned-ON appliance combinations, their energy level disaggregation as well as the presence of UUAs accurately in real households.

Internet of things (IoT) is a smart technology that connects anything anywhere at any time. Such ubiquitous nature of IoT is responsible for draining out energy from its resources. Therefore, the energy efficiency of IoT resources has emerged as a major research issue. In this paper, an energy-efficien t architecture for IoT has been proposed, which consists of three layers, namely, sensing and control, information processing, and presentation. The architectural design allows the system to predict the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for a particular application. The predicted value can be used to boost the utilization of cloud resources by reprovisioning the allocated resources when the corresponding sensory nodes are in sleep mode. This mechanism allows the energy-efficient utilization of all the IoT resources. The experimental results show a significant amount of energy saving in the case of sensor nodes and improved resource utilization of cloud resources.

In this paper, we present a smart service robotic system based on cloud computing services. The design and implementation of infrastructure, computation components and communication components are introduced. The proposed system can alleviate the complex computation and storage load of robots to cloud and provide various services to the robots. The computation components can dynamically allocate resources to the robots. The communication components allow easy access of the robots and provide flexible resource management. Furthermore, we modeled the task-scheduling problem and proposed a max-heaps algorithm. The simulation results demonstrate that the proposed algorithm minimized the overall task costs.

Nowadays, numerous applications of smart home systems provide recommendations for users, including reducing their energy consumption, warnings of defective devices, selecting reliable devices and software, diagnoses, etc [1]. The internet connected, dynamic and heterogeneous nature of the smart home environment creates new security, authentication, and privacy challenges [2]. To solve those challenges, an approach to data privacy in smart home using blockchain technology, which is called smart home based the IoT-Blockchain (SHIB), is proposed in this paper. In order to demonstrate the proposed architecture, an experimental scenario using Ganache, Remix, and web3. js is built among the user, service provider, and smart home to evaluate the performance of the smart contract in the SHIB. Based on the experiment results, the SHIB architecture brings the advantages like data privacy, trust access control, and high extension ability. In addition, the comparison between the proposed architecture and existing models in different parameters such as smart contract, the privacy of data, usage of tokens, updating the policies, and misbehavior judging are performed.

To improve the efficiency in power generation and to reduce the greenhouse gas emission, both Demand Response (DR) strategy and intermittent renewable energy have been proposed or applied in electric power systems. However, the uncertainty and the generation pattern in wind farms and the complexity of demand side management pose huge challenges in power system operations. In this paper, we analytically investigate how to integrate DR and wind energy with fossil fuel generators to (i) minimize power generation cost; (2) fully take advantage wind energy with managed demand to reduce greenhouse emission. We first build a two-stage robust unit commitment model to obtain day-ahead generator schedules where wind uncertainty is captured by a polyhedron. Then, we extend our model to include DR strategy such that both price levels and generator schedule will be derived for the next day. For these two NP-hard problems, we derive their mathematical properties and develop a novel and analytical solution method. Our computational study on a IEEE 118 system with 36 units shows that (i) the robust unit commitment model can significantly reduce total cost and fully make use of wind energy; (ii) the cutting plane method is computationally superior to known algorithms.

Different driving forces push the electricity production towards decentralization. As a result, the current electricity infrastructure is expected to evolve into a network of networks, in which all system parts communicate with each other and influence each other. Multi-agent systems and electronic markets form an appropriate technology needed for control and coordination tasks in the future electricity network. We present the PowerMatcher, a market-based control concept for supply and demand matching (SDM) in electricity networks. In a presented simulation study is shown that the simultaneousness of electricity production and consumption can be raised substantially using this concept. Further, we present a field test with medium-sized electricity producing and consuming installations controlled via this concept, currently in preparation.

Feeding of the world in the 21st century is the biggest challenge, especially for smart farm business. The smart farm has used agriculture automation system instead of traditional agriculture. Traditional agricultural methods employed by the local people are highly sustainable, although the all inclusive cost is not cheap. Our research goal is to provide long term sustainable solution for automation of agriculture. Agriculture automation has several methods to getting data from vegetable crop like sensor for environmental measurement. Therefore, we developed a portable measurement technology including soil moisture sensor, air humidity sensor and air temperature sensor. Moreover, irrigation system using wireless sensor network has installed these sensors, with the purpose for collecting the environment data and controlling the irrigation system via smart phone. The purpose of the experiment is to find better ways of controlling an irrigation system with automatic system and manual control by smart phone. In order to control an irrigation system, we have developed the communication methodology of the wireless sensor network for collected environment data and sending control command to turn on/off irrigation system. It is successful for controlling the irrigation system and controlling the water near the vegetable roots. In this paper, we have attempted to demonstrate the automation of the irrigation system that is useful for farm business which make it comfortable than using traditional agriculture by using smart phone for monitoring and controlling the system. Accordingly, in the long-term has reduced cost as well. The experimental result shows that the accuracy of sending and receiving command control for irrigation system is 96 percent and accuracy of environment collection is 98 percent.


Question: An intelligent load management solution incorporating renewable energy integration for smart home applications.

This paper presents an enhanced decision-support system designed to optimize energy service procurement for residential consumers, particularly within the context of smart homes. The system is centered around a scheduling mechanism that allows homeowners to prioritize and assign values to various energy services, followed by the strategic deployment of their distributed energy resources (DER) to maximize the overall net benefits. Particle swarm optimization (PSO), known for its ease of implementation and efficiency in delivering near-optimal solutions within reasonable computational timeframes, was selected as the core algorithm. To refine the PSO approach, the paper introduces a novel element of stochastic repulsion among particles, thereby enhancing the basic cooperative PSO formulation. The resulting optimized DER schedules are then utilized to assess the additional value that can be gained through the coordinated scheduling of DER. This assessment is conducted by contrasting the costs incurred by end-users when all DER are scheduled in unison using the improved algorithm, against the costs when each DER is scheduled independently. This comparative analysis empowers homeowners to discern whether their specific combination of energy service requirements, available DER, and electricity tariff structures would benefit more from solving the comprehensive coordinated scheduling problem, or if it would be more advantageous to opt for a series of simpler, individual optimizations. This approach not only streamlines energy management but also facilitates the integration of renewable energy sources into smart homes, thereby enhancing sustainability and efficiency.

There are different interesting ways that can be followed in order to reduce costs of grid-connected photovoltaic systems, i.e., by maximizing their energy production in every operating conditions, minimizing electrical losses on the plant, utilizing grid-connected photovoltaic systems not only to generate electrical energy to be put into the power system but also to implement some other important auxiliary services normally implemented by properly conceived apparatus (electrical power quality control, load demand peak control, …), making easy the integration of different kind of renewable electrical energy resources (wind plant, fuel-cells, …).

−A smart charge scheduling model is presented for potential (1) vehicle-to-grid (V2G) enabled battery electric vehicle (BEV) owners who are willing to participate in the grid ancillary services, and (2) grid operators. Unlike most V2G implementations, which are considered from the perspective of power grid systems, this model includes a communication network architecture for connecting system components that supports both BEV owners and grid operators to efficiently monitor and manage the charging and ancillary service activities. This model maximizes the net profit to each BEV participant while simultaneously satisfying energy demands for his/her trips. The performance of BEVs using the scheduling model is validated by estimating optimal annual financial benefits under different scenarios. An analysis of popular BEV models revealed that one of the existing BEVs considered in the study can generate an annual regulation profit of $454, $394 and $318 when the average daily driving distance is 20 miles, 40 miles and 60 miles, respectively. All popular BEV models can completely compensate the energy cost and generate a positive net profit, through the application of the scheduling model presented in this paper, with an annual driving distance of approximately 15,000 miles. Simulation analysis indicated that the extra load distribution from the optimized BEV charging operations were well balanced compared to the unmanaged BEV

We first review some of the suggested methods for energy disaggregation. We then provide a real-life data set for researchers to help developing novel energy disaggregation algorithms. We also present a publicly available experimental data set captured from a typical building on the University of California, Berkeley campus.

Internet of things (IoT) is a smart technology that connects anything anywhere at any time. Such ubiquitous nature of IoT is responsible for draining out energy from its resources. Therefore, the energy efficiency of IoT resources has emerged as a major research issue. In this paper, an energy-efficien t architecture for IoT has been proposed, which consists of three layers, namely, sensing and control, information processing, and presentation. The architectural design allows the system to predict the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for a particular application. The predicted value can be used to boost the utilization of cloud resources by reprovisioning the allocated resources when the corresponding sensory nodes are in sleep mode. This mechanism allows the energy-efficient utilization of all the IoT resources. The experimental results show a significant amount of energy saving in the case of sensor nodes and improved resource utilization of cloud resources.

To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic and its hardware components are not energy-proportional, since it cannot precisely scale its usage to match its supply. Instead, the system must choose when to satisfy its energy demands based on its current energy reserves and predictions of its future energy supply. In this paper, we explore the use of weather forecasts to improve a system's ability to satisfy demand by improving its predictions. We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a wind or solar energy harvesting prediction, and quantify its accuracy. We evaluate our model for both energy sources in the context of two different energy harvesting sensor systems with inelastic demands: a sensor testbed that leases sensors to external users and a lexicographically fair sensor network that maintains steady node sensing rates. We show that using weather forecasts in both wind- and solar-powered sensor systems increases each system's ability to satisfy its demands compared with existing prediction strategies.

Microgrids are operated by a customer or a group of customers for having a reliable, clean and economic mode of power supply to meet their demand. Understanding the economics of system is a prime factor which really depends on the cost/kWh of electricity supplied. This paper presents an easy and simple method for analyzing the dispatch rate of power. An isolated microgrid with solar and wind is considered in this paper. Generation cost functions are modeled with the inclusion of investment cost and maintenance cost of resources. Economic dispatch problem is solved using the reduced gradient method. The effects on total generation cost, with the inclusion of wind energy and solar energy into a microgrid is studied and found the most profitable solution by considering different practical scenarios. The paper gives a detailed correlation between the cost function, investment cost, lifetime and the fluctuant energy forecasting of wind and solar resources. It also discusses the advantages of including the renewable energy credits for the solar panel.

In this paper we present a use case related to the intelligent processing of events coming from the conventional ("cheap") sensors in order to support better energy consumption in commercial buildings. The approach has been implemented using our iCEP framework and deployed in the office space of a real working environment. This research is a kind of the proof of the concept for a new technology that the industry partner would like to exploit.
 The results are very encouraging: smart decisions for the efficient usage of energy can be made by the intelligent processing of "cheap" sensor events.

In a smart distribution power grid, cost efficient and reliable communication architecture plays a crucial role in achieving complete functionality. There are different sets of Quality of Services (QoS) requirements for different data packets transmitting inside the microgrid (a regionally limited smart distribution grid), making it challenging to derive optimal communication architecture. The objective of this research work is to determine the optimal communication technologies for each data packet based on its QoS requirement. In this paper, we have proposed an architecture for a smart distribution power grid with Cyber Physical System enabled microgrids, which accommodate almost all functional requirements of a smart distribution power grid. For easy transition towards optimal communication architecture, we have presented a six-tier communication topology, which is derived from the architecture for a smart distribution power grid. The optimization formulations for each packet structure presented in this paper minimize the overall cost and consider the QoS requirements for each packet. Based on the simulation results, we have made recommendations for optimal communication technologies for each packet and thereby developed a heterogeneous communication architecture for

A Non-Intrusive Load Monitoring (NILM) method, robust even in the presence of unlearned or unknown appliances (UUAs) is presented in this paper. In the absence of such UUAs, this NILM algorithm is capable of accurately identifying each of the turned-ON appliances as well as their energy levels. However, when there is an UUA or set of UUAs are turned-ON during a particular time window, proposed NILM method detects their presence. This enables the operator to detect presence of anomalies or unlearned appliances in a household. This quality increases the reliability of the NILM strategy and makes it more robust compared to existing NILM methods. The proposed Robust NILM strategy (RNILM) works accurately with a single active power measurement taken at a low sampling rate as low as one sample per second. Here first, a unique set of features for each appliance was extracted through decomposing their active power signal traces into uncorrelated subspace components (SCs) via a high-resolution implementation of the Karhunen-Loeve (KLE). Next, in the appliance identification stage, through considering power levels of the SCs, the number of possible appliance combinations were rapidly reduced. Finally, through a Maximum a Posteriori (MAP) estimation, the turned-ON appliance combination and/or the presence of UUA was determined. The proposed RNILM method was validated using real data from two public databases: Reference Energy Disaggregation Dataset (REDD) and Tracebase. The presented results demonstrate the capability of the proposed RNILM method to identify, the turned-ON appliance combinations, their energy level disaggregation as well as the presence of UUAs accurately in real households.

This paper introduces an energy management strategy based on fuzzy logic supervisory for road electric vehicle, combining a fuel cell power source and two energy storage devices, i.e., batteries and ultracapacitors. The control strategy is designed to achieve the high-efficiency operation region of the individual power source and to regulate current and voltage at peak and average power demand, without compromising the performance and efficiency of the overall system. A multiple-input power electronic converter makes the interface among generator, energy storage devices, and the voltage dc-link bus. Classical regulators implement the control loops of each input of the converter. The supervisory system coordinates the power flows among the power sources and the load. The paper is mainly focused on the fuzzy logic supervisory for energy management of a specific power electronic converter control algorithm. Nevertheless, the proposed system can be easily adapted to other converters arrangements or to distributed generation applications. Simulation and experimental results on a 3-kW prototype prove that the fuzzy logic is a suitable energy management control strategy.


Question: A new 60 GHz wideband antenna using coupled half-mode/quarter-mode substrate integrated waveguide technology.

This paper presents a pioneering work on a low-cost, integrated 60-GHz smart antenna receiver subsystem, entirely based on substrate integrated waveguide (SIW) technology. The subsystem is designed to achieve switched-beam functionality, and its components include a slot antenna, a 4x4 Butler matrix network, a bandpass filter, a sub-harmonically pumped mixer, and a local oscillator (LO) source, all operating at 60 GHz. A unique approach is taken in this study by integrating the antenna array, the Butler matrix, and the bandpass filter into a single substrate. To reduce costs, a 30-GHz LO source is utilized instead of a 60-GHz one, driving a 60-GHz sub-harmonically pumped mixer. The 30-GHz LO circuit is comprised of a 10-GHz SIW voltage-controlled oscillator and a frequency tripler. The smart receiver subsystem's operation involves down-converting four 60-GHz signals from the 4x4 Butler matrix, comparing the resulting intermediate frequency (IF) signals in a digital processor based on the criterion of maximum received power, and feeding back control signals to a single-pole four-throw switch array. This allows for beam tuning, ensuring it points toward the main beam of the transmit antenna, thereby effectively tracking the arriving 60-GHz RF signal. The performance of all components is validated through experimental measurements, and the integrated smart receiver subsystem, with all front-end components, is found to yield satisfactory results.

In this letter, a dual-band circular patch antenna is introduced. The antenna consists of a circular patch with a direct feed through microstrip feedline designed to radiate at 2.45 GHz with fractional bandwidth of 4.5%. A circular slot is inserted into the ground plane that radiates by capacitive coupling between the patch and the ground plane. This slot radiates at 1.95 GHz with fractional impedance bandwidth of 5%. The antenna achieves good radiation characteristics by inserting a reflecting plane at optimum position behind it. The antenna has gain of 8.3 and 7.8 dBi at 1.95 and 2.45 GHz, respectively. This antenna is proposed for the rectenna; then it is designed to direct the main beam in a certain direction by increasing front-to-back (F/B) ratio with low cross-polarization levels by using defected reflector structure in the reflecting plane. The equivalent circuit of the proposed antenna is introduced to model the electrical behavior of the antenna. The proposed antenna can be used to harvest the energy from Wi-Fi and widely spread mobile networks. The proposed antenna was designed using CST Microwave Studio. The simulated and measured results show good agreement.

A novel substrate integrated waveguide (SIW) multibeam antenna based on the parabolic reflector principle is proposed and implemented at 37.5 GHz, which takes the advantages of low loss, low profile, high gain, and mass producible. A prototype of the proposed antenna with seven input ports generating a corresponding number of output beams is fabricated in a standard PCB process. The measurements are in good agreement with the simulations, and the results demonstrate that this type of printed multibeam antenna is a good choice for communications applications where mobility and high gain are simultaneously required.

A dual circularly polarized (CP) waveguide array antenna is presented for Ka-band wireless communication. A novel triple-resonant cavity is proposed to implement mode conversion and impedance matching in the <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula>-element subarray. A ridge waveguide polarizer is integrated to form the left-hand circular polarization (LHCP) and the right-hand circular polarization (RHCP). A flyover-shaped full-corporate feeding network is designed to accommodate the dual polarizations and keep the wideband characteristic for a large-scale array. Subarray spacing is used to optimize the port-to-port isolation and decrease the sidelobe level at some definite angles. Computer numerically controlled machining technology is applied to fabricating a <inline-formula> <tex-math notation="LaTeX">$16 \times 16$ </tex-math></inline-formula>-element prototype. Measured results demonstrate a bandwidth of 16% from 27.6 to 32.4 GHz for the dual polarizations, in terms of good reflection coefficient and axial ratio. A maximum gain of 32.8 dBic is achieved at 28.8 GHz. A total efficiency over 60% is obtained throughout the above band for both LHCP and RHCP ports.

A high gain wideband U-shaped patch antenna with two equal arms on poly tetra fluoro ethylene (PTFE) substrate is presented. An inverted U-shaped slot is introduced on the circular or square shaped ground plane just under the U-shaped patch. In this communication the effect of size and shape of the ground plane on impedance bandwidth is studied. Maximum impedance bandwidth of 86.79% (4.5–11.4 GHz) is obtained with circular shaped ground plane with diameter 36 mm. The highest gain achieved is 4.1 dBi. The simulated results are confirmed experimentally. The proposed antenna is simple in structure compared to the regular stacked or coplanar parasitic patch antennas. It is highly suitable for wireless communications.

It is a well known fact that the traditional 1-18 GHz double ridge guide horn (DRGH) antenna suffers from pattern deterioration above 12 GHz. At these frequencies, instead of maintaining a single main lobe radiation pattern, the pattern splits up into four lobes. It was shown in the literature that higher order modes are causing the pattern breakup. A benchmark study is performed to establish the performance of typical current and historic 1-18 GHz DRGH antennas. The performance of the antennas are evaluated in terms of gain, VSWR and radiation patterns. An improved 1-18 GHz DRGH antenna is presented. The new design has better gain and VSWR performance without any pattern deterioration. It also consists of significantly fewer parts, reducing the possibility of performance deterioration due to gaps between parts. Two prototypes of the new design were manufactured and tested with excellent agreement between measured and simulated results. The aperture dimensions of the new design are identical to that of the traditional DRGH, making it the only 1-18 GHz DRGH without pattern breakup whose aperture dimensions comply with the requirements specified in MIL-STD-461F - 24.2 by 13.6 cm.

A novel compact microstrip fed dual-band coplanar antenna for wireless local area network is presented. The antenna comprises of a rectangular center strip and two lateral strips printed on a dielectric substrate and excited using a 50 Omega microstrip transmission line. The antenna generates two separate resonant modes to cover 2.4/5.2/5.8 GHz WLAN bands. Lower resonant mode of the antenna has an impedance bandwidth (2:1 VSWR) of 330 MHz (2190-2520 MHz), which easily covers the required bandwidth of the 2.4 GHz WLAN, and the upper resonant mode has a bandwidth of 1.23 GHz (4849-6070 MHz), covering 5.2/5.8 GHz WLAN bands. The proposed antenna occupy an area of 217 mm2 when printed on FR4 substrate (epsivr=4.7). A rigorous experimental study has been conducted to confirm the characteristics of the antenna. Design equations for the proposed antenna are also developed

This work proposed a compact multiple-input multiple-output (MIMO) antenna system having four antennas each fed by coplanar waveguide (CPW) operating for 3.5GHz band (3400–3600) MHz. The proposed MIMO antenna system is realized with two groups of printed Inverted L-monopole of size 5×4 mm2 placed orthogonally to each other to attain the diversity capability of polarization and pattern at the edge of non-ground portion of size 15 × 15 mm2 of mobile handset. The proposed MIMO antenna system consists of four monopole inverted-L shape radiator each with parasitic shorted stripe extending from the ground plane having same dimensions. The parasitic inverted-L shorted striped served as tuning element by reducing the resonating frequency of the monopole radiator and improving the impedance matching. The mutual coupling between the closely packed antennas is addressed by the neutralization line by feeding anti-phase current to the neighbor antenna. The simulation result showed that that the impedance bandwidth at S11 ≤ −10 dB is 200 MHz enough for 5G network communication channel. The diversity parameters of interest are envelop correlation coefficient and mean effective gain are also calculated.

This proposed paper, a single band microstrip patch antenna for 5G wireless application is presented. This proposed antenna is suitable for the millimeter wave frequency. The single band antenna consist of new H slot and E slot loaded on the radiating patch with the 50 ohms microstrip line feeding used. This single band antenna is simulated on a Rogers RT5880 dielectric substrate have relative permittivity 2.2, loss tangent 0.0009, and height 1.6mm. The antenna is simulated by Electromagnetic simulation, computer software technology Microwave studio. The proposed single band antenna and simulated result on return loss, VSWR, surface current and 3D radiation pattern is presented. The simulated antenna shows the return loss −40.99dB at 60 GHz millimeter wave 5G wireless application presented.

This paper describes the design and modeling of CMOS transistors, integrated passives, and circuit blocks at millimeter-wave (mm-wave) frequencies. The effects of parasitics on the high-frequency performance of 130-nm CMOS transistors are investigated, and a peak f/sub max/ of 135 GHz has been achieved with optimal device layout. The inductive quality factor (Q/sub L/) is proposed as a more representative metric for transmission lines, and for a standard CMOS back-end process, coplanar waveguide (CPW) lines are determined to possess a higher Q/sub L/ than microstrip lines. Techniques for accurate modeling of active and passive components at mm-wave frequencies are presented. The proposed methodology was used to design two wideband mm-wave CMOS amplifiers operating at 40 GHz and 60 GHz. The 40-GHz amplifier achieves a peak |S/sub 21/| = 19 dB, output P/sub 1dB/ = -0.9 dBm, IIP3 = -7.4 dBm, and consumes 24 mA from a 1.5-V supply. The 60-GHz amplifier achieves a peak |S/sub 21/| = 12 dB, output P/sub 1dB/ = +2.0 dBm, NF = 8.8 dB, and consumes 36 mA from a 1.5-V supply. The amplifiers were fabricated in a standard 130-nm 6-metal layer bulk-CMOS process, demonstrating that complex mm-wave circuits are possible in today's mainstream CMOS technologies.

This paper, for the first time, presents successful integration of a W-band antenna with an organically flip-chip packaged silicon-germanium (SiGe) low-noise amplifier (LNA). The successful integration requires an optimized flip-chip interconnect. The interconnect performance was optimized by modeling and characterizing the flip-chip transition on a low-loss liquid crystal polymer organic substrate. When the loss of coplanar waveguide (CPW) lines is included, an insertion loss of 0.6 dB per flip-chip-interconnect is measured. If the loss of CPW lines is de-embedded, 0.25 dB of insertion loss is observed. This kind of low-loss flip-chip interconnect is essential for good performance of W-band modules. The module, which we present in this paper, consists of an end-fire Yagi-Uda antenna integrated with an SiGe BiCMOS LNA. The module is 3 mm × 1.9 mm and consumes only 19.2 mW of dc power. We present passive and active E- and H-plane radiation pattern measurements at 87, 90, and 94 GHz. Passive and active antennas both showed a 10-dB bandwidth of 10 GHz. The peak gain of passive and active antennas was 5.2 dBi at 90 GHz and 21.2 dBi at 93 GHz, respectively. The measurements match well with the simulated results.