Short-term Load Forecasting Method for Power System Based on Adaptive Neural Fuzzy Inference System

被引：0
|
作者：
Li, Song [1 ]
Wang, Jie-sheng [1 ]
Wang, Min-wei [1 ]
机构：
[1] Univ Sci & Technol Liaoning, Sch Elect & Informat Engn, Anshan 114044, Peoples R China
来源：
PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020) | 2020年
关键词：
Power System; Load Forecasting; Adaptive Neural Fuzzy Inference System; ANFIS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The operation of the modern electric power system is usually associated with various operational planning procedures. The power system needs to meet the specified technical performance standards and economic performance standards so as to meet the power load. A short-term load forecasting model based on adaptive neural fuzzy inference system is established. Based on Sugeno-type fuzzy neural network model, three kinds of output parameters optimization algorithm (Kaczmarz algorithm, Gradient algorithm and Kalman algorithm) are adopted. The simulation experiments results show the adaptive neural fuzzy inference system based on the Kalman method has the optimal forecasting precision on the short-term load for the power system.
引用
收藏
页码：2920 / 2925
页数：6
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