Smart Energy Management

Kaile Zhou received his B.S. degree and Ph.D. degrees from Hefei University of Technology, Hefei, China in 2010 and 2014 respectively. He was a visiting scholar at the University of Arizona, Tucson, AZ, USA, and a Postdoctoral Research Fellow at the City University of Hong Kong, Hong Kong SAR, China. He is now Professor of Management Science and Engineering at Hefei University of Technology. His research interests include energy system optimization, integrated energy services, and data-driven decision-making.  Lulu Wen received his B.S. degree from the School of Transportation and Management, Dalian Maritime University, Dalian, China in 2016, and the Ph.D. degree from the School of Management, Hefei University of Technology, Hefei, China in 2021. He was a visiting scholar at the Lawrence Berkeley National Laboratory from 2019 to 2020. He is now an engineer at Hithink RoyalFlush Information Network Co., Ltd., Hangzhou, China. His current research interests include big data analytics and smart energy management.

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.