A Review of Load Forecasting of the Distributed Energy System

4th International Conference on Advances in Energy Resources and Environment Engineering

能源学;生态环境科学

Wang, Zhenyu^1 ; Li, Jun^1 ; Zhu, Shilin^2 ; Zhao, Jun^2 ; Deng, Shuai^2 ; Zhong, Shengyuan^2 ; Yin, Hongmei^2 ; Li, Hao^2 ; Qi, Yan^2 ; Gan, Zhiyong^3

National Electricity Science Research Institute (Wuhan) of Energy Efficiency Evaluation, Wuhan

430074, China^1

Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Tianjin University, Ministry of Education of China, Tianjin

300350, China^2

State Grid Tianjin Electric Power ScienceandResearch Institute, Tianjin

300202, China^3

关键词: Distributed energy systems;    Evaluation indicators;    Global intelligence;    Input parameter;    Load forecasting;    Load forecasting model;    Occupant behavior;    Research trends;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/237/4/042019/pdf
DOI  :  10.1088/1755-1315/237/4/042019

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

With the development of global intelligence industry and new energy systems, the role of load forecasting is increasingly prominent. This article reviews the research progress and applications of load forecasting technology. How to improve the accuracy and speed of load forecasting is the current research hotspot, and this paper comprehensively summarizes the influencing factors for the performance of load forecasting such as various input parameters and load forecasting models. Besides, this paper reviews evaluation indicators of load forecasting, three different sizes' buildings as typical cases. Finally, three research trends in this field are summed up: deep learning, predictive measurement, and combination with occupant behavior prediction.