BP神经网络在计及气温因素的短期日负荷预测中的应用

Application of BP Neural Network in Short-term Load Forecast Considering Temperature Factor

随着社会经济的快速发展，空调及取暖负载在总用电负荷中的比重日益增加，气温变化引起的负荷波动趋势越来越明显。通过分析2006年6～8月份金华市总用电负荷和气温数据，提出在传统短期负荷预测方法上增加气温数据作为辅助输入变量，对于提高短期日负荷预测精度、增加电力系统调度效率以及缓解区域供电不足等方面具有重要意义。

Along with the fast developing economy, the proportion of the air conditioning and heating load in the whole load increase day by day, the load undulation tendency caused by the temperature change is obvious more and more. This article analyzes the load and the temperature data in Jinhua from June to August in 2006. Propose adding the temperature data into the traditional short - term load forecast as the assistant input variable. This method has the vital significance in enhancing the precision of short - term forecast, increasing the efficiency of power system dispatch, as well as alleviating the insufficiency in region power supply and so on.