基于确定性退火方法的短期负荷预测

A DETERMINISTIC ANNEALING APPROACH FOR SHORT TERM LOAD FORECASTING

首次将确定性退火方法用于短期负荷预测领域。该方法利用分片回归模型逼近预测函数 ,并通过引入“熵函数”限制模型规模。其优点是模型简洁灵活、预测能力强并具有良好的全局最优性能 ,从而克服了目前正在使用的前向网、径向基函数网、分类回归树及多重自适应样条和模糊逻辑等方法分划欠灵活且易于陷入局部极值点的缺陷。实际算例表明 ,对于短期负荷预测问题 。

This paper presents a new algorithm for short term load forecasting based on the deterministic annealing technique. The forecasting function is a piecewise model whose complexity is limited by the Shannon entropy of the partition. The advantages of this method include: flexibility, less model complexity, high forecasting accuracy and global optimal property. The new approach can be used to alleviate the restriction for the partitions and the tendency to be trapped in local minimum of the existing methods such as multilayer neural networks, radial basis function networks, classification and regression trees, multiple adaptive regression splines and fuzzy logic method. The practical numerical tests for short term load forecasting problems show that the prediction accuracy achieved by deterministic annealing method is significantly higher than that obtained by multilayer neural networks and radial basis function networks.