Short-Term Solar Power Prediction Using an RBF Neural Network

Authors

Jianwu Zeng, University of Nebraska–LincolnFollow
Wei Qiao, University of Nebraska–LincolnFollow

Date of this Version

2011

Citation

2011 IEEE Power and Energy Society General Meeting, doi: 10.1109/PES.2011.6039204

Comments

Copyright IEEE 2011. Used by permission.

Abstract

This paper proposes a radial basis function (RBF) neural network-based model for short-term solar power prediction (SPP). Instead of predicting solar power directly, the model predicts transmissivity, which is then used to obtain solar power according to the extraterrestrial radiation. The proposed model uses a novel two-dimensional (2D) representation for hourly solar radiation and uses historical transmissivity, sky cover, relative humidity and wind speed as the input. Simulation studies are carried out to validate the proposed model for shortterm SPP by using the data obtained from the National Solar Radiation Database (NSRDB). The performance of the RBF neural network is compared with that of two linear regression models, i.e., an autoregressive (AR) model and a local linear regression (LLR) model. Results show that the RBF neural network significantly outperforms the AR model and is better than the LLR model. Furthermore, the use of transmissivity and other meteorological variables, especially the sky cover, can significantly improve the SPP performance.