Fuzzy-Rule based Load Pattern Classifier for Short-Tern Electrical Load Forecasting

Based on the knowledge of historical data sets, a fuzzy rule-based classifier for electrical load pattern classification is set up. Considering with the accuracy and interpretation of fuzzy rules, multi-objective genetic algorithm are applied to choose the Pareto optimum rules that are used to classify electrical load. In the computation experiments, the generated fuzzy rule-based classifier is used to load forecasting, the computation results show that it leads to high classification performance, and it can supply more sufficient and effective historical data for load forecasting, better performance of load forecasting is gained accordingly.