气象要素在电力负荷预测中的应用

Application of Meteorological Factors to Load Forecasting Based on ANN

综合应用人工神经网络、模糊理论等智能技术,着重考虑天气因素对电力负荷的影响,确定了一种有效的电力系统短期负荷预测方法。并应用陕西省9个地市1998～2001年的逐日8个气象要素以及对应的逐日电力负荷值,对陕西省电力负荷进行训练和预测,研究结果证明这种方法能较大地提高日负荷预测的精度。

Based on Artificial Neuron Network (ANN) and Fuzzy theory, an efficient method of short-term load forecasting is put forward. In it, the influences of meteorological factors on load forecasting are also concerned. The method applies eight meteorological factors day by day in nine cities and corresponding load value day by day of Shaanxi Province from 1999 to 2001 to train and forecast load of Shaanxi Province. The results show that the new method can largely improve the precision of load forecasting.