EPNN-based prediction of meteorological data for renewable energy systems

In this paper, an application of an Evolving Polynomial Neural Network (EPNN) for prediction of meteorological time series (global solar irradiation, air temperature, relative humidity, and wind speed) is described. Prediction of such data plays a very important role in design of the renewable energy systems. The problem of time series prediction is formulated as a system identification problem, where the input of the system is the past values (y (t - 1), y (t - 2), y (t - 3), …) of a time series and its desired output (y (t), y (t + 1), y (t + 2), …) are the future of a time series. In this study, a dataset of meteorological time series for five years collected in Algiers (Algeria) by the National Office of Meteorology has been used. The obtained results showed a good agreement between both series, measured and predicted. The correlation coefficient ( r ) is arranged between 0.9821 and 0.9923, the mean relative error over the whole data set is not exceed 15.4 %. The proposed model provides more accurate results than other ANN’s architecture, wavenet (wavelet-network) and Adaptive Neuro-Fuzzy Inference Scheme (ANFIS). In order to show the effectiveness of the proposed predictor, the predicted data have been used for sizing, and prediction of the output energy of photovoltaic systems.