摘要： 在电力市场中边际电价预测得准确与否,对于发电厂的竞价决策具有非常关键的影响。为了克服BP神经网络自身算法的缺陷,得到更高的学习精度和更快的收敛速度,该文建立了基于遗传算法的小波神经网络电价预测模型。该模型结合了遗传算法的全局优化搜索能力以及小波神经网络良好的时频局部性质。经实例验证该模型能有效地提高预测精度,避免了BP神经网络的固有缺陷。

关键词: 电力市场, 电价预测, 遗传算法, 小波神经网络

Abstract: The accuracy of the electricity price forecasting is very important to power plants bidding decision. In order to avoid the limitation of the BP neural networks, this paper establishes the electricity price forecasting model using wavelet neural networks based on the genetic algorithm. The model combines the global optimization searching performance of the genetic algorithm and the time-frequency localization of the wavelet neural networks. The example shows that this model can effectively improve the forecasting precision and avoid the limitation of the BP neural networks model.

中图分类号: 

• TP183