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According to its various features, the solar photovoltaics (PV) system is realized as a significant promising energy source to cope with energy shortcomings and environmental impacts like contamination. Therefore, it is mandatory to estimate and predict the output power for prediction intervals to avoid any power outage or urgent disturbances in the utility grid. These are challenging tasks as the solar PV output power depends on the weather variables data such as temperature and solar radiation. In this article, the estimation and forecast of solar PV output power are investigated with an upscaling method using three different types of artificial neural networks (ANNs) in order to reduce the estimation errors in current types of ANNs. The multilayer feedforward neural network (MLFFNN), recurrent neural network (RNN), and nonlinear autoregressive exogenous (NARX) model neural network (NARXNN) are applied to estimate and forecast the total output power of four real solar PV substations in Egypt. Hence both the surface temperature and the solar radiation of each PV substation are applied as the inputs of each designed NN, whereas the total output power of the four PV substations is its output. For the training and effectiveness investigation procedures of each applied ANNs, the data of two months (60 days) are attained and collected from these four PV substations. Here, the data of the first 45 days are applied to train the three designed NNs, while the data from the remaining 15 days, which are not applied for the training, are used to check the effectiveness and the generalization capability of the trained NNs. Hence, the estimation process is considered a prior step for the forecast of the output power. Therefore, an upscaling method is utilized for assessing and forecasting a regional solar PV output power because of the limited number of monitored plants and applied data. The results provide evidence that the trained NNs are running very well and efficiently to estimate the power correctly. The performance of the MLFFNN is the best compared with the other NNs, whereas the NARXNN’s performance is the lowest one. The MLFFNN achieves the lowest mean squared error (MSE) of 0.27533 and the lowest absolute approximation error of 0.2099 MWh. Finally, the assessment and comparison among the three trained NNs and other techniques in recently published articles are highlighted and presented which reveal the performance superiority of the three trained NNs compared to other ANNs.

Solar photovoltaics (PV) is considered an auspicious key to dealing with energy catastrophes and ecological contamination. This type of renewable energy is based on climatic conditions to produce electrical power. In this article, a multilayer feedforward neural network (MLFFNN) is implemented to predict and forecast the output power for a solar PV power station. The MLFFNN is designed using the module temperature and the solar radiation as the two main only inputs, whereas the expected power is its output. Data of approximately one week (6-days) are obtained from a real PV power station in Egypt. The data of the first five days are used to train the MLFFNN. The training of the designed MLFFNN is executed using two types of learning algorithms: Levenberg-Marquardt (LM) and error backpropagation (EBP). The data of the sixth day, which are not used for the training, are used to check the efficiency and the generalization capability of the trained MLFFNN by both algorithms. The results provide evidence that the trained MLFFNN is running very well and efficiently to predict the power correctly. The results obtained from the trained MLFFNN by LM (MLFFNN-LM) are compared with the corresponding ones obtained by the MLFFNN trained by EBP (MLFFNN-EBP). From this comparison, the MLFFNN-LM has slightly lower performance in the training stage and slightly better performance in the stage of effectiveness investigation compared with the MLFFNN-EBP. Finally, a comparison with other previously published approaches is presented. Indeed, predicting the power correctly using the artificial NN is useful to avoid the fall of the power that maybe happen at any time. [ABSTRACT FROM AUTHOR]