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.

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