摘要
日电力负荷预测是电力市场运营的基本内容。当前大多数预测方法对不同时段往往采用相同的预测模型和算法,而较少考虑不同时段的负荷组成及特征变化。提出了一种新的分时段多模型组合预测方法。根据负荷组成和特征变化,将日96点负荷分为多个时间段,每个时段内采用多元线性回归、灰色预测、支持向量机和神经网络预测等子模型加权实现多模型组合预测。通过对华东某地市电网日负荷96点曲线的预测结果显示,该方法效果较好,日预测均方根误差在1.78%以内,能较好地满足实际电力系统的负荷预测要求。
Daily load forecasting is a basic role of power market.Most of load forecasting methods use one same model in one day,regardless of the change of load composing and characteristic at different time segments.A new segmented multi-model combining load forecasting strategy was proposed in this paper.According to different load composing and characteristic,96 points daily load was separated into many time segments.At each time segment,a multi-model combining load forecasting,composed by multivariate linear regression,grey prediction,SVM and neural network forecasting,was used to forecast load.The forecasting results of a city in east China showed that,the MSE forecasting error of 96 points daily load is only about 1.78%.The method can satisfy the request of real power system well.
出处
《计算机系统应用》
2011年第3期98-101,共4页
Computer Systems & Applications
基金
浙江省自然科学基金(Y1090182)
关键词
日电力负荷预测
多模型组合预测
分时段
权
daily load forecasting
multi-model combining forecasting
time segments
power
