基于树结构Parzen估计器优化集成学习的短期负荷预测方法

TPE-Based Boosting Short-Term Load Forecasting Method

短期负荷预测主要用于电力系统实时调度、日前发电计划的制定,对电力系统经济调度、系统的安全运行具有重要意义.国内外在采用智能模型进行短期负荷预测方面开展了大量研究,然而智能预测方法的预测效果较易受到现存方法结构及参数的影响,以及预测对象自身个性差异使得参数难以复用,如何精准快速地获取方法结构与参数成为短期负荷预测的关键难题.对此,提出基于树结构Parzen估计器优化集成学习的短期负荷预测方法,可对方法结构与参数进行快速寻优.将该方法应用于中国南方某省短期负荷预测,以实际算例验证了其对预测精度的有效提升.

Short-term load forecasting is generally applied in power system real-time dispatching and day-ahead generation planning,which is of great significance for power system economic dispatching and safe operation of the system.Many researches on short-term load forecasting using smart models have been conducted at home and abroad.However,how to obtain the optimal structure and parameters accurately and quickly poses a challenge to short-term load forecasting,because the prediction performance of smart forecasting methods is more easily affected by the structure and parameters of the method,and the personality difference of the prediction object itself makes it difficult for the parameters to be reused.Aiming at this problem,a tree-structured Parzen estimator(TPE)-based boosting short-term load forecasting method is proposed.The results show that the proposed method can achieve rapid optimization of structure and parameters,which is verified in the application in short-term load forecasting of a southern province in China to improve the prediction accuracy.