摘要
为了更有效地提高地区电网短期负荷预测的精度,提出基于BP人工神经网络原理,利用神经网络高度非线性建模能力,根据电力系统短期负荷变化的特性,建立了既反映电力系统负荷连续性、周期性及其负荷变化趋势,又包含天气因素变化对系统负荷影响的天气因素敏感模型,并对岳阳地区短期负荷进行预测,预测结果表明天气因素应用于电力系统短期负荷预测后使预测精度明显提高,故这种方法是可行和有效的。
In order to improve the accuracy of short-term load forecast for local power nets, a sensitive model, which not only reflects the load continuity, periodicity and load variation trend of power system, but also includes the influence of weather factors on the system load, was established on the basis of principle of BP artificial neural network （ANN） and by means of the nonlinear modeling ability of ANN. The short term load in Yueyang power system was forecasted. The forecas ting results show that the forecasting accuracy was obviously improved when weather factors was applied to short-term load forecasting of power system. Therefore, this method is feasible and effective.
出处
《现代电子技术》
2011年第6期185-187,共3页
Modern Electronics Technique
基金
西安文理学院专项科研基金资助项目(自然科学)(kcy200816)
