考虑多时间尺度数据的中长期负荷预测方法

Medium and Long-term Load Forecasting Method Considering Multi-time Scale Data

精确的负荷预测对电力系统至关重要。智能电网数据庞大、统计口径不同、结构复杂,影响负荷的因素复杂多变,较多影响因素数据分布在不同时间尺度上,需考虑多尺度数据间的依赖性,充分利用不同时间尺度的数据才能做出准确且稳定可靠的中长期预测。该文提出一种堆叠长短期记忆(long-termandshort-termmemory,LSTM)网络模型,将不同时间尺度的历史负荷数据、地区经济数据和气候数据信息整合到模型中,为防止神经网络的过拟合,对神经网络中除偏置参数以外的全部参数进行正则化处理。以广东电网的真实负荷数据进行算例分析。实验结果表明,该方法所提出的堆叠长短期记忆循环神经网络模型与单一神经网络模型和堆叠支持向量机模型相比,有效提高了负荷预测准确率。

Accurately load forecasting is critical to power system.Massive smart grid data with complex structures is collected from different electrical installations.The factors affecting the load are complicated.Most of them are distributed on different time scales.It is necessary to consider the dependence between multi-scale data and make full use of data from different time scales in order to make accurate,stable and reliable medium and long-term predictions.This paper proposed a stacked long-term and short-term memory(LSTM)network model,which integrated three different time scales data of monthly historical load,annual regional economic and daily climate information.In the model,in order to avoid the algorithm is overfitting,all the parameters excepted the offset parameters which in the circulatory neural network were regularized.An example of Guangdong load forecasting proved that compared to the method using the support vector machine(SVM)and deep neural networks(DNN)method,the accuracy of load forecasting is effectively improved.