Comprehensive Load Forecast Based on GM(1，1) and BP Neural Network

doi:10.12204/j.issn.1000-7229.2020.05.009

Abstract

Abstract: Aiming at the limitation of the single forecasting model in power load forecast， this paper proposes a combined model based on BP neural network and GM (1，1) residual correction to improve the precision of load forecasting model. The algorithm combination is used to model the system. First， the GM (1，1) model is used for prediction to obtain the gray residual sequence， which is used to establish the BP residual correction model. The model is used for residual prediction. Finally， the residual correction value and GM (1，1) are used. The model predicted values are superimposed to obtain the finally required load predicted value. The model is verified in a simulate case， whose results show that the modified model has high prediction accuracy and practicability.

Key words: load forecast, GM(1, 1), BP neural network, combined forecast, residual correction

CLC Number:

TM 715

SONG Jian， SHU Hongchun， DONG Jun， LIANG Yuting， LI Yulong， YANG Bo. Comprehensive Load Forecast Based on GM(1，1) and BP Neural Network[J]. Electric Power Construction, 2020, 41(5): 75-80.

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