基于相似日和SAE-DBiLSTM模型的短期电力负荷预测  

Short-term Power Load Forecasting Using Similar Day and SAE-DBiLSTM Model

作　　者：姜东良 李天昊 刘文浩 JIANG Dongliang;LI Tianhao;LIU Wenhao(Faculty of Electric and Control Engineering,Liaoning Technical University,Huludao 125105)

机构地区：[1]辽宁工程技术大学电气与控制工程学院,葫芦岛125105

出　　处：《电气工程学报》2022年第4期240-249,共10页Journal of Electrical Engineering

基　　金：国家自然科学基金(51974151);辽宁省自然科学基金指导(20180550308)资助项目。

摘　　要：提高短期电力负荷预测精度有助于电力公司高效地管理能源和更加经济可靠地运行。随着信息通信技术在电力系统的广泛应用,可获得的电力系统数据迅速增多,为数据驱动的电力负荷预测提供了数据基础,但这些数据通常结构性较差且特征不明确。由此,提出了基于相似日和SAE-DBiLSTM模型的短期电力负荷预测方法。首先,对获得的电力负荷数据进行预处理,并利用栈式自编码网络无监督提取由相似日、基准日负荷数据和天气信息构成的数据隐含用电特征;再将所得的隐含用电特征输入深度双向长短期记忆网络(Deep bi-directional long short-term memory,DBi LSTM)进行训练学习;最后用2016年全国大学生电工数学建模竞赛数据集,将所提模型与其他模型进行对比测试(包括DBiLSTM、SAE-ELM、SAE-DGRU、SAE-DLSTM和SAE-DBiLSTM)。试验结果表明,SAE-DBiLSTM组合模型在不同地区均具有更高的预测精度,该方法简单可靠且能更好地预测短期区域电力负荷。Accurate short-term load forecasting can guarantee efficient energy management and reliable operation for electric power companies.With the extensive application of information and communication technology in the power system,the available data of power system increases day after day,providing enough data for accurate power load forecasting.However,these data are usually lack of structure and obvious features.To solve the problem,a short-term power load forecasting method based on similar day and SAE-DBi LSTM model is proposed.Firstly,the electric power data is preprocessed and the power consumption features are described by similar day,base day load data and weather information,abstract features are further extracted using stack self-coding network(SAE).Secondly,these abstract features are input into the deep bidirectional long and short-term memory network(DBiLSTM)for training of the prediction model.Finally,the performance of the proposed model and several other models are compared,including DBi LSTM,SAE-ELM,SAE-DGRU,SAE-DLSTM and SAE-DBi LSTM,using the dataset of 2016 National College Student Electrical Mathematical Modeling Contest.Experimental results show that the proposed SAE-DBiLSTM model achieved the highest prediction performance in different regions.Moreover,the proposed model is simple and reliable for forecasting short-term regional power load.

关 键 词：相似日 栈式自编码器(SAE) BiLSTM网络 短期电力负荷预测 

分 类 号：TM715[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]