基于改进灰色预测模型的电力需求预测研究(英文)

Research on Electricity Demand Forecasting Based on Improved Grey Prediction Model

当建模序列具有随机振荡特征时灰色预测模型的模拟及预测精度较差,实际上序列越光滑模型的模拟精度就越高;本文通过改善建模序列的光滑性以提高灰色预测模型的模拟精度,研究了一种压缩随机振荡序列振幅的算法,推导了基于随机振荡序列的灰色预测模型^x(t)=Fβt-31-(-1)tE-T;最后应用该模型预测我国西部某城市的电力需求,并与其他灰色预测模型的模拟精度进行了比较(新模型的模拟精度为7%,其他模型的精度均为12%),表明新模型具有更好的模拟效果,研究成果对丰富与完善灰色预测理论体系,促进灰色模型与电力需求预测的对接,具有积极意义。

Simulation precision of conventional grey prediction models is poor when modeling sequence has the feature of oscillation. Actually, the smoother the sequence is, the higher the simulation precision is. With the purpose o{ perfecting the smoothness of oscillation sequence and improving the simulation precision of grey models, this paper researches a smoothing algorithm which can compress the amplitude of oscillation sequence, and by this algorithm, deduces a novel grey prediction model based on oscillation sequence, that is T. Finally, we employ the new model to forecast the electricity demand of a city in western China, and compare the simulation preci- sion with other grey models（the simulation error of the new model is 7 ,others are all 12 ）, the results shows the new model has the best simulation effect. Research findings in this paper have an important significance to enrich and perfect grey system theory and construct a more reasonable electricity demand forecasting model.