电力系统自动化2018，Vol.42Issue(5)：133-139,7.DOI:10.7500/AEPS20170826002
基于深度信念网络的短期负荷预测方法
Short-term Load Forecasting Based on Deep Belief Network
摘要
Abstract
The development of power system informationization and the increasing integration of distributed generators and electric vehicle to distribution network have increased the complexity of power consumption mode and put forward higher requirements for the accuracy and stability of load forecasting.A short-term load forecasting method based on deep belief network is proposed.The method includes the network construction,the layer-by-layer pre-training of the model parameters, the supervised fine-tuning,and the application of the model.In the pre-training process of the model parameters,the Gaussian-Bernoulli restricted Boltzmann machine(GB-RBM)is used as the first module for stacking the deep belief network to deal more effectively with the multi-type real-valued input data.And the partially supervised training algorithm combined by unsupervised training algorithm and supervised training algorithm is used for pre-training.The Levenberg-Marquardt(LM)optimization algorithm is used to fine-tune the parameters obtained by the pre-training phase,which can help to converge faster to the optimal solution.Finally,the actual load data are used for test and the experiments results show that the method proposed has higher prediction accuracy in the case of large training samples and complicated load factors.关键词
电力系统/负荷预测/受限玻尔兹曼机/深度信念网络/列文伯格-马夸尔特算法Key words
power system/load forecasting/restricted Boltzmann machines/deep belief network/Levenberg-Marquardt algorithm引用本文复制引用
孔祥玉,郑锋,鄂志君,曹旌,王鑫..基于深度信念网络的短期负荷预测方法[J].电力系统自动化,2018,42(5):133-139,7.基金项目
国家自然科学基金资助项目(51377119) （51377119）
国家重点研发计划资助项目(2017YFB0902902).This work is supported by National Natural Science Foundation of China(No.51377119)and National Key R&D Program of China(No.2017YFB0902902). （2017YFB0902902）
