文章摘要
孔祥玉,郑锋,鄂志君,等.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. DOI: 10.7500/AEPS20170826002.
KONG Xiangyu,ZHENG Feng,E Zhijun, et al.Short-term Load Forecasting Based on Deep Belief Network[J].Automation of Electric Power Systems,2018,42(5):133-139. 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
DOI:10.7500/AEPS20170826002
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
KeyWords: power system  load forecasting  restricted Boltzmann machines  deep belief network  Levenberg-Marquardt algorithm
上网日期:2018-01-23
基金项目:国家自然科学基金资助项目(51377119);国家重点研发计划资助项目(2017YFB0902902)
作者单位E-mail
孔祥玉 智能电网教育部重点实验室(天津大学), 天津市 300072  
郑锋 智能电网教育部重点实验室(天津大学), 天津市 300072 zhengfeng6_6@163.com 
鄂志君 国网天津市电力公司, 天津市 300010  
曹旌 国网天津市电力公司, 天津市 300010  
王鑫 国网天津市电力公司, 天津市 300010  
摘要:
      电力系统信息化的发展及配电网中分布式电源和电动汽车的大量接入,增加了用电模式的复杂性,对负荷预测的精确度和稳定性提出了更高的要求。提出了一种基于深度信念网络的短期负荷预测方法。该方法包括深度信念网络的构建、模型参数的逐层预训练和微调,以及模型的应用等步骤。在模型参数预训练过程中,采用高斯—伯努利受限玻尔兹曼机(GB-RBM)作为堆叠组成深度信念网络的第1个模块,使其能够更有效地处理对负荷有影响的多类型实值输入数据;并采用无监督训练和有监督训练相结合的部分有监督训练算法进行预训练;利用列文伯格—马夸尔特(LM)优化算法微调预训练阶段得到的网络参数,使其更快收敛于最优解。最后,以实际负荷数据进行算例分析,结果表明,在训练样本较大且负荷影响因素复杂的情况下,所提方法具有更高的预测精度。
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.
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