Medium-term Load Forecasting Method with Improved Deep Belief Network for Renewable Energy

Abstract

With the continuous transition of the traditional power system to the newpower system, the composition of the power generation side in the powersystem has gradually begun to be dominated by renewable energy (at leastmore than 50%). Among the renewable energy sources, wind power is themost susceptible to weather and environmental influences. These factorsincrease the complexity of the power generation mode, and put forwardhigher requirements for the accuracy and stability of load forecasting. Thispaper proposes a medium-term renewable energy load forecasting methodbased on an improved deep belief network (IDBN-NN). The method includesthe construction of a deep belief network, the layer-by-layer pre-trainingand fine-tuning of model parameters, and the application of the model.In the process of model parameter pre-training, Gauss-Bernoulli RestrictedBoltzmann Machine (GB-RBM) is used as the first part of the stacked deepbelief network, so that it can process multiple types of real-valued input data more effectively. In addition, IDBN-NN uses a combination of unsupervisedtraining and supervised training for pre-training. Finally, the actual load datais used to analyze the calculation example. When the number of RBM layersis 3, the number of fully connected layers is 1, and Dropout is equal to0.2, the MSE and loss values are optimal, which are 0.0037 and 0.0104,respectively. The experimental results show that the proposed method hashigher prediction accuracy when the training sample is large and the loadinfluencing factors are complex.