Medium-term Load Forecasting Method with Improved Deep Belief Network for Renewable Energy
With the continuous transition of the traditional power system to the new power system, the composition of the power generation side in the power system has gradually begun to be dominated by renewable energy (at least more than 50%). Among the renewable energy sources, wind power is the most susceptible to weather and environmental influences. These factors increase the complexity of the power generation mode, and put forward higher requirements for the accuracy and stability of load forecasting. This paper proposes a medium-term renewable energy load forecasting method based on an improved deep belief network (IDBN-NN). The method includes the construction of a deep belief network, the layer-by-layer pre-training and fine-tuning of model parameters, and the application of the model. In the process of model parameter pre-training, Gauss-Bernoulli Restricted Boltzmann Machine (GB-RBM) is used as the first part of the stacked deep belief network, so that it can process multiple types of real-valued input data more effectively. In addition, IDBN-NN uses a combination of unsupervised training and supervised training for pre-training. Finally, the actual load data is used to analyze the calculation example. When the number of RBM layers is 3, the number of fully connected layers is 1, and Dropout is equal to 0.2, the MSE and loss values are optimal, which are 0.0037 and 0.0104, respectively. The experimental results show that the proposed method has higher prediction accuracy when the training sample is large and the load influencing factors are complex.
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