基于基因表达式程序设计及误差循环补偿的电力系统短期负荷预测

Short-term Load Forecasting Based on Gene Expression Programming With Error Cycling Compensation

将基因表达式程序设计(gene expression programming,GEP)算法应用于电力系统短期负荷预测中,并提出误差循环补偿模型,得到了较高的预测精度。预测过程先对负荷样本进行常规和滤波处理,消除伪数据,然后运用基因表达式程序设计的灵活表达能力,把不同日、同一时刻的负荷序列作为样本,对未来时刻的负荷进行分时短期预测。得到预测模型后,计算其与样本数据的误差,再把误差值作为样本进行演化,并把误差模型补偿到原模型上,如果达不到预测要求,则循环计算模型误差进行演化,直到满足要求为止。结果表明基因表达式程序设计算法具有较高的效率,且误差循环补偿模型能够有效补偿演化过程中的误差。经比较,基于基因表达式程序设计及其误差循环补偿的预测模型比时间序列和遗传程序设计算法具有更好的预测效果。

An attempt to apply gene expression programming （GEP） to short-term load forecasting is made, where the error recycling compensation model is suggested and higher forecasting precision is gained. In order to eliminate the pseudo-data, the load samples are filtered and processed generally first, then the load series of the same time but different days are chosen as the training samples, and by means of the flexible expressive capacity of GEP, the models of different time points are evolved according to time-sharing. Then the errors between forecasting models and samples are evolved by means of GEP as well, and finally, the error compensation models are compensated to the former corresponding forecasting models. And the error compensation models will not be evolved until the results are satisfied. According to forecasting results, it indicates that GEP is of high efficiency and the error recycling compensation models can compensate the errors of evolutionary process. After comparison with the results forecasted by means of time series and genetic programming （GP）, it proves that the algorithm of GEP in short-term load forecasting is better.