Smart energy management : data driven methods for energy service innovation / Kaile Zhou, Lulu Wen.
Zhou, Kaile, author.; Wen, Lulu, author.
2022
TK3091 .Z56 2022
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Title Smart energy management : data driven methods for energy service innovation / Kaile Zhou, Lulu Wen.
Author Zhou, Kaile, author.
ISBN 9789811693601 (electronic bk.)
9811693609 (electronic bk.)
9789811693595
9811693595
DOI https://doi.org/10.1007/978-981-16-9360-1
Published Singapore : Springer, [2022]
Copyright ©2022
Language English
Description 1 online resource (317 pages) : illustrations (chiefly color)
Item Number 10.1007/978-981-16-9360-1 doi
Call Number TK3091 .Z56 2022
Dewey Decimal Classification 621.319
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Description based upon print version of record.
Added Author Wen, Lulu, author.
Available in Other Form Smart Energy Management
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Chapter 1 Introduction
Chapter 2 Residential Electricity Consumption Pattern Mining based on Fuzzy Clustering
Chapter 3 Load Profiling Considering Shape Similarity using Shape-based Clustering
Chapter 4 Load Classification and Driven Factors Identification based on Ensemble Clustering
Chapter 5 Power Demand and Probability Density Forecasting based on Deep Learning
Chapter 6 Load Forecasting of Residential Buildings based on Deep Learning
Chapter 7 Incentive-based Demand Response with Deep Learning and Reinforcement Learning
Chapter 8 Residential Electricity Pricing based on Multi-Agent Simulation
Chapter 9 Integrated Energy Services based on Integrated Demand Response
Chapter 10 Electric Vehicle Charging Scheduling Considering Different Charging Demands
Chapter 11 P2P Electricity Trading Pricing in Energy Blockchain Environment
Chapter 12 Credit-Based P2P Electricity Trading in Energy Blockchain Environment.

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