一种基于RBF自适应神经模糊推理的短期电力负荷预测方法

Short-term Load Forecasting Method Based on RBF Adaptive Neural Fuzzy Inference

根据东莞电网的历史负荷数据,分析该地区电力负荷的特征,综合分析天气、温度、日期等因素对电力负荷预测的影响.针对负荷具有一定的客观规律,但又具有很大的随机性和不确定性,提出了一种新型基于径向基函数的自适应神经模糊推理的方法进行短期负荷预测.用MATLAB编制电力系统短期负荷预测程序,并绘制预测结果曲线.结果表明基于RBF自适应神经模糊推理的预测精度是令人满意的,验证了本方法的有效性和实用性.

According to the historical load data of Dongguan grid,power load characteristic in this area is analyzed and the load forecasting influence factors such as the date type,temperature,weather conditions are also analyzed.In view of the load has a certain objective laws,it also has a lot of randomness and uncertainty,a neural fuzzy inference to carry on short-term load forecasting based on RBF is proposed.The program that carries on short-term power system load forecasting is designed in MATLAB,forecasting result curves are drawn.The results indicate that the RBF adaptive neural fuzzy inference of the forecast accuracy is satisfied,so the given method is effective and practical.