Affiliation:
1. Northeast Electric Power University, Jilin 132000, China
2. State Grid Jilin Electric Power Research Institute, Changchun 130000, China
3. State Grid Jilinsheng Electric Power Supply Company, Changchun 130000, China
Abstract
With the construction of a new-type power system under the China “double carbon” target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time.
Funder
Department of Science and Technology of Jilin Province
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology,Modeling and Simulation