Grey Verhulst Power Load Forecasting Method Based on Background Value Optimization

2018 International Conference on Civil, Architecture and Disaster Prevention

土木建筑工程

Huang, Zonghong^1 ; Dang, Dongsheng^1 ; Gao, Chuncheng^2 ; Wang, Lei^2

State Grid Ningxia Electric Power Eco-Tech Research Institute, Yinchuan, Ningxia Province

750004, China^1

Beijing Kedong Electric Power Control System CO. LTD, Beijing

100192, China^2

关键词: Adaptive particle swarm optimization algorithm;    Background value;    Electricity-consumption;    Grey Verhulst model;    Load sequences;    Optimization ability;    Power load forecasting;    Prediction accuracy;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/218/1/012104/pdf
DOI  :  10.1088/1755-1315/218/1/012104

学科分类：土木及结构工程学

来源: IOP

【 摘 要 】

According to the nonlinearity and uncertainty of the load sequence, the grey Verhulst model (GV) adapted to the "S" type growth is used to predict the future electricity consumption of Ningxia. This paper analyzes the application limitations of the traditional grey Verhulst model, and introduces the background value of the vector α modified GV model, thus constructing a more universal background value modified GV model, applying the global optimization ability of adaptive particle swarm optimization algorithm (APSO) to solve the optimal α value. a grey Verhulst model (APSO-GV) based on adaptive particle swarm optimization algorithm is proposed. The case study shows that the model has high prediction accuracy and universality.