One-hour Ahead Solar Irradiance/Power Forecasting Using Radial Basis Function Neural Network with Fuzzy Activation Function

Due to increasing awareness of global warming, solar photovoltaics have received much attention However, solar photovoltaic power generation is associated with intermittence and uncertainty as a result of the meteorological conditions. Accordingly, accurate predictions of the power output from photovoltaic arrays is important for the efficient operation of power systems. This paper presents a supervised learning-based Radial Basis Function Neural Network (RBFNN) neural network for 1-hour ahead solar irradiance/power forecasting. The proposed RBFNN employed double-Gaussian functions as its basis functions, which are typeII fuzzy activation functions. It was found that the type-II fuzzy activation is able to deal with the uncertainty of data. The genetic algorithm was used to optimize the weighting/bias parameters as well as two means/standard deviations of each double Gaussian function. In order to explore the performance of the proposed RBFNN, three structures of RBFNNs are examined: parallel, cascaded and separated RBFNNs. From the simulation results, it was found that the proposed parallel RBFNN outperforms the cascaded and separated RBFNNs. Moreover, the proposed RBFNN with double-Gaussian activation functions attains better accuracy than traditional multi-layer feedforward neural network and RBFNN with single-Gaussian activation functions.