A Short-term Load Forecasting Method Based on Fuzzy Neural RBF Network Adaptive Control

2nd International Symposium on Application of Materials Science and Energy Materials

材料科学;能源学

Wang, Xiaokan^1 ; Wang, Qiong^1

Henan Mechanical and Electrical Vocational College, Xinzheng

451191, China^1

关键词: Forecast accuracy;    Fuzzy-neural;    Load forecasting;    Network adaptive;    Power system loads;    RBF(radial basis function);    Short term load forecast;    Short term load forecasting;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/490/4/042041/pdf
DOI  :  10.1088/1757-899X/490/4/042041

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

According to historical load data of the power grid in a certain area, by which analyze this area's power load characteristic and consider the load forecasting influence factors such as the date type, temperature, weather conditions in the first. In view of the load has a certain objective laws, but which has a lot of randomness and uncertainty, applying one kind new based on the RBF (Radial Basis Function) Neural Fuzzy Inference to carry on short-term load forecasting. By programming with MATLAB to carry on short-term power system load forecasting, carry on the short-term load forecast experiment to the practical grid and draw the forecasting result curves. The result indicated that the RBF Adaptive Neural Fuzzy Inference of the forecast accuracy is satisfied with the verification of this method is effective and practical.