Short–mid-term solar power prediction by using artificial neural networks

Solar irradiation is one of the major renewable energy sources and technologies related with this source have reached to high level applications. Prediction of solar irradiation shows some uncertainties depending on atmospheric parameters such as temperature, cloud amount, dust and relative humidity. These conditions add new uncertainties to the prediction of this astronomical parameter. In this case, prediction of generated electricity by photovoltaic or other solar technologies could be better than directly solar irradiation.In this paper, firstly, Artificial Neural Networks (ANNs) methodology is applied to data obtained from a 750 W power capacity of solar PV panel. The main objective of this paper is to determine time horizon having the highest representative for generated electricity prediction of small scale solar power system applications. It is seen that 5 min time horizon gives the best solar power prediction for short term and 35 min could be used for medium terms in April. In addition, these time horizons have increased to 3 and 40 min for very short time and medium time prediction respectively during August. During April and August Root Mean Square Errors (RMSEs) between measured and testing values changed between 33–55 W and 37–63 W ranges respectively. Especially, during August for solar irradiation, stationary conditions are observed and these situations let ANN predict easily generated electricity from 30 to 300 min ahead.

► In this paper, ANN methodology is applied to data obtained from a 750 Wp of solar PV panel.
► It is seen that 5-min data give the best prediction and 40-min could be used for medium terms in April.
► In April and August, MAPE between measured and testing values decreased to 2% and 1% respectively.
► Especially, during August for solar irradiation, stationary conditions are observed.
► These situations let ANN predict easily generated electricity from 30 to 300 min ahead.