电力系统自动化2019，Vol.43Issue(1)：73-80,8.DOI:10.7500/AEPS20180629013
基于协整-格兰杰因果检验和季节分解的中期负荷预测
Medium-term Load Forecasting Based on Cointegration-Granger Causality Test and Seasonal Decomposition
摘要
Abstract
In recent years, with the transformation of national economy, great changes have taken place in the economic structure of China.The prediction based on the historical data of electric power load will cause great error.In order to solve the problem which traditional load forecasting method is not enough for economic and meteorological factors, a forecasting method for medium-term load is proposed.This method can consider the influence of economy, climate and other factors.First, using seasonal decomposition, the monthly electricity consumption of history is decomposed into long-term and cycle component, seasonal component and irregular component, and the relationship between economic factors and long-term trend and cyclic components of electricity consumption is analyzed by cointegration test and Granger causality test in econometrics.The key indexes to influence the prediction of electric quantity is determined.Each component is predicted by support vector machine (SVM) based on electricity, meteorology and economic data, and the monthly total quantity of electricity is predicted.Finally, the effectiveness and feasibility of the method are illustrated by an example.关键词
中期负荷预测/季节分解/协整检验/格兰杰因果检验/支持向量机Key words
medium-term load forecasting/seasonal decomposition/cointegration test/Granger causality test/support vector machine (SVM)引用本文复制引用
刘俊,赵宏炎,刘嘉诚,潘良军,王楷..基于协整-格兰杰因果检验和季节分解的中期负荷预测[J].电力系统自动化,2019,43(1):73-80,8.基金项目
国家自然科学基金资助项目(51507126) （51507126）
陕西省重点研发计划资助项目(2017ZDCXL-GY-02-03) This work is supported by National Natural Science Foundation of China (No. 51507126) and Key R&D Program of Shaanxi Province (No. 2017ZDCXL-GY-02-03). （2017ZDCXL-GY-02-03）
