Download or read book Data driven Approaches to Load Modeling Andmonitoring in Smart Energy Systems written by Guoming Tang and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In smart energy systems, load curve refers to the time series reported by smart meters, which indicate the energy consumption of customers over a certain period of time. The widespread use of load curve (data) in demand side management and demand response programs makes it one of the most important resources. To capture the load behavior or energy consumption patterns, load curve modeling is widely applied to help the utilities and residents make better plans and decisions. In this dissertation, with the help of load curve modeling, we focus on data-driven solutions to three load monitoring problems in different scenarios of smart energy systems, including residential power systems and datacenter power systems and covering the research fields of i) data cleansing, ii) energy disaggregation, and iii) fine-grained power monitoring. First, to improve the data quality for load curve modeling on the supply side, we challenge the regression-based approaches as an efficient way to load curve data cleansing and propose a new approach to analyzing and organizing load curve data. Our approach adopts a new view, termed portrait, on the load curve data by analyzing the inherent periodic patterns and re-organizing the data for ease of analysis. Furthermore, we introduce strategies to build virtual portrait datasets and demonstrate how this technique can be used for outlier detection in load curve. To identify the corrupted load curve data, we propose an appliance-driven approach that particularly takes advantage of information available on the demand side. It identifies corrupted data from the smart meter readings by solving a carefully-designed optimization problem. To solve the problem efficiently, we further develop a sequential local optimization algorithm that tackles the original NP-hard problem by solving an approximate problem in polynomial time. Second, to separate the aggregated energy consumption of a residential house into that of individual appliances, we propose a practical and universal energy disaggregation solution, only referring to the readily available information of appliances. Based on the sparsity of appliances' switching events, we first build a sparse switching event recovering (SSER) model. Then, by making use of the active epochs of switching events, we develop an efficient parallel local optimization algorithm to solve our model and obtain individual appliances' energy consumption. To explore the benefit of introducing low-cost energy meters for energy disaggregation, we propose a semi-intrusive appliance load monitoring (SIALM) approach for large-scale appliances situation. Instead of using only one meter, multiple meters are distributed in the power network to collect the aggregated load data from sub-groups of appliances. The proposed SSER model and parallel optimization algorithm are used for energy disaggregation within each sub-group of appliances. We further provide the sufficient conditions for unambiguous state recovery of multiple appliances, under which a minimum number of meters is obtained via a greedy clique-covering algorithm.Third, to achieve fine-grained power monitoring at server level in legacy datacenters, we present a zero-cost, purely software-based solution. With our solution, no power monitoring hardware is needed any more, leading to much reduced operating cost and hardware complexity. In detail, we establish power mapping functions (PMFs) between the states of servers and their power consumption, and infer the power consumption of each server with the aggregated power of the entire datacenter. We implement and evaluate our solution over a real-world datacenter with 326 servers. The results show that our solution can provide high precision power estimation at both the rack level and the server level. In specific, with PMFs including only two nonlinear terms, our power estimation i) at the rack level has mean relative error of 2.18%, and ii) at the server level has mean relative errors of 9.61% and 7.53% corresponding to the idle and peak power, respectively.

Download or read book Smart Energy Management written by Kaile Zhou and published by Springer Nature. This book was released on 2022-02-04 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a relatively whole view of data-driven decision-making methods for energy service innovation and energy system optimization. Through personalized energy services provision and energy efficiency improvement, the book can contribute to the green transformation of energy system and the sustainable development of the society. The book gives a new way to achieve smart energy management, based on various data mining and machine learning methods, including fuzzy clustering, shape-based clustering, ensemble clustering, deep learning, and reinforcement learning. The applications of these data-driven methods in improving energy efficiency and supporting energy service innovation are presented. Moreover, this book also investigates the role of blockchain in supporting peer-to-peer (P2P) electricity trading innovation, thus supporting smart energy management. The general scope of this book mainly includes load clustering, load forecasting, price-based demand response, incentive-based demand response, and energy blockchain-based electricity trading. The intended readership of the book includes researchers and engineers in related areas, graduate and undergraduate students in university, and some other general interested audience. The important features of the book are: (1) it introduces various data-driven methods for achieving different smart energy management tasks; (2) it investigates the role of data-driven methods in supporting various energy service innovation; and (3) it explores energy blockchain in P2P electricity trading, and thus supporting smart energy management.

Download or read book Data Driven Synthetic Load Modeling for Smart City Energy Management Studies written by Fernando Bereta dos Reis and published by . This book was released on 2020 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Intelligent Data Driven Modelling and Optimization in Power and Energy Applications written by B Rajanarayan Prusty and published by CRC Press. This book was released on 2024-05-09 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include: an exclusive section on essential preprocessing approaches for the data-driven model a detailed overview of data-driven model applications to power system planning and operational activities specific focus on developing forecasting models for renewable generations such as solar PV and wind power, and showcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworks This book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.

Download or read book Scalable Data driven Modeling and Analytics for Smart Buildings written by Srinivasan Iyengar and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Buildings account for over 40% of the energy and 75% of the electricity usage. Thus, by reducing our energy footprint in buildings, we can improve our overall energysustainability. Further, the proliferation of networked sensors and IoT devices in recent years have enabled monitoring of buildings to provide data at various granularity. For example, smart plugs monitor appliance level usage inside the house, while solar meters monitor residential rooftop solar installations. Furthermore, smart meters record energy usage at a grid-scale. In this thesis, I argue that data-driven modeling applied to the IoT data from a smart building, at varying granularity, in association with third party data can help to understand and reduce human energy consumption. I present four data-driven modeling approaches - that use sophisticated techniques from Machine Learning, Optimization, and Time Series Analysis - applied at different granularities. First, I study IoT devices inside the house and discuss an approach called NIMD that automatically models individual electrical loads found in a household. The analytical model resulting from this approach can be used in several applications. For example, these models can improve the performance of NILM algorithms to disaggregate loads in a given household. Further, faulty or energy-inefficient appliances can be identified by observing deviations in model parameters over its lifetime. Second, I examine data from solar meters and present a machine learning framework called SolarCast to forecast energy generation from residential rooftop installations. The predictions enable exploiting the benefits of locally-generated solar energy. Third, I employ a sensorless approach utilizing a graphical model representation to report city-scale photovoltaic panel health and identify anomalies in solar energy production. Immediate identification of faults maximizes the solar investment by aiding in optimal operational performance. Finally, I focus on grid-level smart meter data and use correlations between energy usage and external weather to derive probabilistic estimates of energy, which is leveraged to identify the least efficient buildings from a large population along with the underlying cause of energy inefficiency. The identified homes can be targeted for custom energy efficiency programs.

Download or read book Smart Meter Data Analytics written by Yi Wang and published by Springer Nature. This book was released on 2020-02-24 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporated into consumer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scale. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems.

Download or read book Applications of Big Data and Artificial Intelligence in Smart Energy Systems written by Neelu Nagpal and published by CRC Press. This book was released on 2023-09-29 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the era of propelling traditional energy systems to evolve towards smart energy systems, including power generation, energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, domestic loads, and industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution, automation, energy regulation & control, and energy trading. This book covers the applications of various big data analytics,artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies for modern power systems such as the Internet of Things, Blockchain for smart home and smart city solutions in depth. Technical topics discussed in the book include: • Hybrid smart energy system technologies • Smart meters • Energy demand forecasting • Use of different protocols and communication in smart energy systems • Power quality and allied issues and mitigation using AI • Intelligent transportation • Virtual power plants • AI based smart energy business models • Smart home solutions • Blockchain solutions for smart grids.

Download or read book Big Data Analysis for Smart Electrical Energy Distribution Systems written by Mohan Kolhe and published by Academic Press. This book was released on 2022-04-15 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analysis for Smart Electrical Energy Distribution Systems covers the application of big data analytics and techniques with selective applications for the operation, analysis, planning and design of future electrical distribution systems. The book provides data-driven applications in smart distribution systems, machine learning techniques for renewable energy predictions, and load forecasting examples for intelligent techno-economic operation and control of the network as a microgrid. This title gives those within this multidisciplinary field a comprehensive look at machine learning techniques for renewable energy prediction, demand forecasting, and intelligent techno-economic operation and control of distributed energy systems. With electricity networks changing rapidly due to the increased integration of intermittent and variable power generation from renewable energy sources, mismatch between the supply and demand of electricity is also on the rise. Hence, the use of new renewables is a widely discussed topic. Presents a systematic and integrated reference on data-driven applications for solving the problems of electrical energy distribution network topologies using smart energy meter data Provides a comprehensive look at the machine learning techniques available for renewable energy prediction, demand forecasting, and intelligent techno-economic operation and control of distributed energy systems Features specific data driven approaches for demand side management with grid constraints and the development of dynamic electrical energy pricing

Download or read book Handbook of Smart Energy Systems written by Michel Fathi and published by Springer Nature. This book was released on 2023-08-04 with total page 3382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook analyzes and develops methods and models to optimize solutions for energy access (for industry and the general world population alike) in terms of reliability and sustainability. With a focus on improving the performance of energy systems, it brings together state-of-the-art research on reliability enhancement, intelligent development, simulation and optimization, as well as sustainable development of energy systems. It helps energy stakeholders and professionals learn the methodologies needed to improve the reliability of energy supply-and-demand systems, achieve more efficient long-term operations, deal with uncertainties in energy systems, and reduce energy emissions. Highlighting novel models and their applications from leading experts in this important area, this book will appeal to researchers, students, and engineers in the various domains of smart energy systems and encourage them to pursue research and development in this exciting and highly relevant field.

Download or read book Towards Energy Smart Homes written by Stephane Ploix and published by Springer Nature. This book was released on 2021-11-11 with total page 627 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book exemplifies how smart buildings have a crucial role to play for the future of energy. The book investigates what already exists in regards to technologies, approaches and solutions both with a scientific and technological point of view. The authors cover solutions for mirroring and tracing human activities, optimal strategies to configure home settings, and generating explanations and persuasive dashboards to get occupants better committed in their home energy managements. Solutions are adapted from the fields of Internet of Things, physical modeling, optimization, machine learning and applied artificial intelligence. Practical applications are given throughout.

Download or read book Modeling Analysis and Control of Smart Energy Systems written by Naoui, Mohamed and published by IGI Global. This book was released on 2024-08-08 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing demand for cleaner and more intelligent energy solutions poses a challenge that resonates across academic, engineering, and policymaking spheres. The complexity of integrating renewable energy sources, energy storage solutions, and advanced communication technologies demands a comprehensive understanding, rigorous analysis, and innovative control strategies. The academic community, in particular, seeks a guiding light through this intricate maze of evolving energy dynamics. Modeling, Analysis, and Control of Smart Energy Systems is a groundbreaking publication that offers more than theoretical exploration; it is a roadmap equipped with the knowledge and tools required to shape the future of energy systems. From laying conceptual foundations to unraveling real-world case studies, the book seamlessly bridges the gap between theory and application. Its comprehensive coverage of mathematical modeling, dynamic system analysis, intelligent control strategies, and the integration of renewable energy sources positions it as an authoritative reference for researchers, engineers, and policymakers alike.

Download or read book Machine Learning Approaches to Non Intrusive Load Monitoring written by Roberto Bonfigli and published by Springer Nature. This book was released on 2019-11-01 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Analytics in Power Markets written by Qixin Chen and published by Springer Nature. This book was released on 2021-10-01 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to solve some key problems in the decision and optimization procedure for power market organizers and participants in data-driven approaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load uncertainty modeling, bus load bad data correction, and monthly load forecasting. The following part of the book answers how much information can be obtained from public data in locational marginal price (LMP)-based markets. It introduces topics such as congestion identification, componential price forecasting, quantifying the impact of forecasting error, and financial transmission right investment. The final part of the book answers how to model the complex market bidding behaviors. Specific works include pattern extraction, aggregated supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants and make essential policies. It will benefit and inspire researchers, graduate students, and engineers in the related fields.

Download or read book Modelling and Simulation of Electrical Energy Systems Through a Complex Systems Approach Using Agent Based Models written by Enrique Alberto Kremers and published by KIT Scientific Publishing. This book was released on 2014-07-31 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complexity science aims to better understand the processes of both natural and man-made systems which are composed of many interacting entities at different scales. A disaggregated approach is proposed for simulating electricity systems, by using agent-based models coupled to continuous ones. The approach can help in acquiring a better understanding of the operation of the system itself, e.g. on emergent phenomena or scale effects; as well as in the improvement and design of future smart grids.

Download or read book Big Data Application in Power Systems written by Reza Arghandeh and published by Elsevier. This book was released on 2017-11-27 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data. The book chapters discuss challenges, opportunities, success stories and pathways for utilizing big data value in smart grids. Provides expert analysis of the latest developments by global authorities Contains detailed references for further reading and extended research Provides additional cross-disciplinary lessons learned from broad disciplines such as statistics, computer science and bioinformatics Focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data

Download or read book Artificial Intelligence Techniques for a Scalable Energy Transition written by Moamar Sayed-Mouchaweh and published by Springer Nature. This book was released on 2020-06-19 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.).

Download or read book Improving Energy Efficiency Through Data Driven Modeling Simulation and Optimization written by Dirk Deschrijver and published by Mdpi AG. This book was released on 2021-05-31 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems.