Abstract
In this paper, an efficient method is proposed to deal with photovoltaic generation output forecasting with Deep Boltzmann Machine. In recent years, the penetration of photovoltaic generation has been widely spread in the world due to clean energy. However, it has brought about uncertainties for generation schedules in a way that power system operators have to consider the significant variations of generation output. As a result, the forecasting model of the generation output with high accuracy is required in the industries. This paper proposes a Deep Neural Network (DNN) model that integrates Deep Boltzmann Machine with Generalized Radial Basis Function Network (GRBFN) of Artificial Neural Network (ANN). The proposed model is tested for real data of photovoltaic generation output.
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