Short-term Load Forecasting Based on Deep Belief Network

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. © 2018 Automation of Electric Power Systems Press.