Short-term solar power prediction using a support vector machine

This paper proposes a least-square (LS) support vector machine (SVM)-based model for short-term solar power prediction (SPP). The input of the model includes historical data of atmospheric transmissivity in a novel two-dimensional (2D) form and other meteorological variables, including sky cover, relative humidity, and wind speed. The output of the model is the predicted atmospheric transmissivity, which then is converted to solar power according to the latitude of the site and the time of the day. Computer simulations are carried out to validate the proposed model by using the data obtained from the National Solar Radiation Database (NSRDB). Results show that the proposed model not only significantly outperforms a reference autoregressive (AR) model but also achieves better results than a radial basis function neural network (RBFNN)-based model in terms of prediction accuracy. The superiority of using transmissivity over sigmoid functions for data normalization is testified. Simulation studies also show that the use of additional meteorological variables, especially sky cover, improves the accuracy of SPP.