Open AccessA Short-term forecasting model of load demand in summer of Chongqing based on BP neural network
Author(s) -
Wenzhe Zhang,
Yan Shi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1861/1/012069
Subject(s) - term (time) , artificial neural network , computer science , power grid , resource (disambiguation) , grid , smart grid , peak load , power (physics) , real time computing , operations research , artificial intelligence , engineering , geography , automotive engineering , electrical engineering , computer network , physics , geodesy , quantum mechanics
Based on the historical load data collected in July and August 2020 in Chongqing, and in view of the characteristics of the regional load in summer Chongqing, a new short-term load forecasting method was put forward in this paper, which is based on BP neural network and takes into account social activities and meteorological factors. The simulation results show that the proposed short-term load forecasting method can accurately realize the daily maximum load forecasting, it provides a theoretical reference for load forecasting and power resource optimization of smart grid in the future.