基于负荷特征提取的神经网络短期负荷预测

综合考虑天气负荷类型和历史数据特征对负荷变化的影响 ,提出了一种新的短期负荷预测方法。通过ARMA、BP神经网络等提取具有特征的神经网络学习样本 ,用反向传播算法建立神经网络短期负荷预测模型。实际算例表明 :该法在负荷平稳或波动较大的季节均有预测精度高且适应性好的特点。

Short-Term Load Forecasting Based on the Neural Networks with Load Characteristics Distilling

According to the features of power load and considering the combined influence of weather, day type and historical load data, a new short-term load forecasting method is put forward in which the learning samples of load are distilled and reorganized with its characteristics by using ARMA and artificial neural networks, a forecast mode gives sufficient ability of processing non-linear problems by neural networks, and the accuracy and adaptability of the forecasting are improved not only for the seasons when loads vary stably and slowly, but also for the seasons when loads fluctuate drastically.