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The sources from which electrical energy is derived are getting exhausted day by day. So, the need has arisen to look for other renewable sources of energy which would meet the increasing demand. This is where the need of employing smart meter in residential houses arises. Smart meter helps householders to understand the pattern of electricity consumption by their houses' appliances and as a result find alternatives to lessen the energy consumption . In the present day a considerable amount of electricity is consumed by the household sector. An awareness among people regarding the consumption pattern will help to reduce electricity consumption . Efficient load monitoring in the metering side of households can improve the household energy management system considerably. In the residential energy monitoring system, the deployment of smart meters will have enormous contribution to the efficient and effective management system of energy. Real-time information, more reliable, secure, about consumers can be accessed by this. Consumers can have information of their energy consumption pattern and thereby use electricity judiciously through the communication between them and utility. The acquisition of data will be faster and more accurate with the concept of smart greed which was not possible earlier on account of lacking real-time information. For collection of real time data residences should be provided with sensors or devices for gathering such information and smart appliances for management and optimization of consumption of energy. Keeping in mind the fact that a smart grid is vital for implementing load monitoring and identification, this chapter deals with the conception regarding smart grid and its effective implementation in residential sector. The identification of loads and its monitoring are also the concerns of this chapter. In recent times, global utility industries are giving much endeavour to address challenges like generation heterogeneity, demand response, reduction in overall emission of carbon and conservation of energy. While conventional power grid, in vogue is unable to address such critical issues, it is expected from smart grid or intelligent grid to cover up the vital deficiency of the power grid used conventionally The rising energy demand and the scarcity of resources have made it extremely important to conserve energy throughout the world. Globally, out of the total energy consumption, consumption of residential energy accounts for a lion's share and it is predicted to rise in the upcoming days. For instance, in the European Union, 40% of the generated electricity is consumed by the residential sector and it is envisaged that the world-wide demand of energy will increase considerably by 2030. In United States too, approximately 35% of the whole energy produced gets consumed by the residential sector. Moreover, this consumption is estimated to rise by 15% by the year 2030 . Apart from energy price hikes and change in climate, the rise in consumption of energy will also affect a country's economy directly. In view of the above, it is very essential to reduce energy consumption, especially at the residential sector, significantly. To achieve this, residential energy consumption monitoring and relaying of data back to the consumers play a significant role.
This book constitutes the refereed proceedings of the 9th International Conference on Ubiquitous Computing, UbiComp 2007. It covers all current issues in ubiquitous, pervasive and handheld computing systems and their applications, including tools and techniques for designing, implementing, and evaluating ubiquitous computing systems; mobile, wireless, and ad hoc networking infrastructures for ubiquitous computing; privacy, security, and trust in ubiquitous and pervasive systems.
The two-volume set LNCS 6593 and 6594 constitutes the refereed proceedings of the 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The 83 revised full papers presented were carefully reviewed and selected from a total of 144 submissions. The second volume includes 41 papers organized in topical sections on pattern recognition and learning, soft computing, systems theory, support vector machines, and bioinformatics.
Research on Smart Grids has recently focused on the energy monitoring issue, with the objective of maximizing the user consumption awareness in building contexts on the one hand, and providing utilities with a detailed description of customer habits on the other. In particular, Non-Intrusive Load Monitoring (NILM), the subject of this book, represents one of the hottest topics in Smart Grid applications. NILM refers to those techniques aimed at decomposing the consumption-aggregated data acquired at a single point of measurement into the diverse consumption profiles of appliances operating in the electrical system under study. This book provides a status report on the most promising NILM methods, with an overview of the publically available dataset on which the algorithm and experiments are based. Of the proposed methods, those based on the Hidden Markov Model (HMM) and the Deep Neural Network (DNN) are the best performing and most interesting from the future improvement point of view. One method from each category has been selected and the performance improvements achieved are described. Comparisons are made between the two reference techniques, and pros and cons are considered. In addition, performance improvements can be achieved when the reactive power component is exploited in addition to the active power consumption trace.
Non-Intrusive Load Monitoring (NILM) is a set of techniques that estimate the electricity usage of individual appliances from power measurements taken at a limited number of locations in a building. One of the key challenges in NILM is having too much data without class labels yet being unable to label the data manually for cost or time constraints. This paper presents an active learning framework that helps existing NILM techniques to overcome this challenge. Active learning is an advanced machine learning method that interactively queries a user for the class label information. Unlike most existing NILM systems that heuristically request user inputs, the proposed method only needs minimally sufficient information from a user to build a compact and yet highly representative load signature library. Initial results indicate the proposed method can reduce the user inputs by up to 90% while still achieving similar disaggregation performance compared to a heuristic method. Thus, the proposed method can substantially reduce the burden on the user, improve the performance of a NILM system with limited user inputs, and overcome the key market barriers to the wide adoption of NILM technologies.
Microgrid technology is an emerging area, and it has numerous advantages over the conventional power grid. A microgrid is defined as Distributed Energy Resources (DER) and interconnected loads with clearly defined electrical boundaries that act as a single controllable entity concerning the grid. Microgrid technology enables the connection and disconnection of the system from the grid. That is, the microgrid can operate both in grid-connected and islanded modes of operation. Microgrid technologies are an important part of the evolving landscape of energy and power systems. Many aspects of microgrids are discussed in this volume, including, in the early chapters of the book, the various types of energy storage systems, power and energy management for microgrids, power electronics interface for AC & DC microgrids, battery management systems for microgrid applications, power system analysis for microgrids, and many others. The middle section of the book presents the power quality problems in microgrid systems and its mitigations, gives an overview of various power quality problems and its solutions, describes the PSO algorithm based UPQC controller for power quality enhancement, describes the power quality enhancement and grid support through a solar energy conversion system, presents the fuzzy logic-based power quality assessments, and covers various power quality indices. The final chapters in the book present the recent advancements in the microgrids, applications of Internet of Things (IoT) for microgrids, the application of artificial intelligent techniques, modeling of green energy smart meter for microgrids, communication networks for microgrids, and other aspects of microgrid technologies. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in the area of microgrids, this is a must-have for any library.
This open access book summarizes the results of the European research project “Twin-model based virtual manufacturing for machine tool-process simulation and control” (Twin-Control). The first part reviews the applications of ICTs in machine tools and manufacturing, from a scientific and industrial point of view, and introduces the Twin-Control approach, while Part 2 discusses the development of a digital twin of machine tools. The third part addresses the monitoring and data management infrastructure of machines and manufacturing processes and numerous applications of energy monitoring. Part 4 then highlights various features developed in the project by combining the developments covered in Parts 3 and 4 to control the manufacturing processes applying the so-called CPSs. Lastly, Part 5 presents a complete validation of Twin-Control features in two key industrial sectors: aerospace and automotive. The book offers a representative overview of the latest trends in the manufacturing industry, with a focus on machine tools.
Focusing on non-intrusive load monitoring techniques in the area of smart grids and smart buildings, this book presents a thorough introduction to related basic principles, while also proposing improvements. As the basis of demand-side energy management, the non-intrusive load monitoring techniques are highly promising in terms of their energy-saving and carbon emission reduction potential. The book is structured clearly and written concisely. It introduces each aspect of these techniques with a number of examples, helping readers to understand and use the corresponding results. It provides latest strengths on the non-intrusive load monitoring techniques for engineers and managers of relevant departments. It also offers extensive information and a source of inspiration for researchers and students, while outlining future research directions.