Photovoltaic power generation depends significantly on solar radiation, which is variable and unpredictable in nature. As a result, the production of electricity from photovoltaic power cannot be guaranteed permanently during the operational phase. Forecasting global solar radiation can play a key role in overcoming this drawback of intermittency. This paper proposes a new hybrid method based on machine learning (ML) algorithms and daily classification technique to forecast 1 h ahead of global solar radiation in the city of Évora. Firstly, several comparative studies have been done between random forest (RF), gradient boosting (GB), support vector machines (SVM), and artificial neural network (ANN). These comparisons were made using annual, seasonal, and daily testing sets in order to determine the best ML algorithm under different meteorological conditions. Subsequently, the daily classification technique has been applied to classify the original training set into sunny and cloudy training subsets in order to enhance the forecasting accuracy. The evaluation of the proposed ML algorithms was carried out using the normalized root mean square error (nRMSE) and the normalized absolute mean error (nMAE). The results of the seasonal comparison show that the RF model performs well for spring and autumn seasons with nRMSE equaling 22.53% and 23.42%, respectively. While the SVR model gives good results for winter and summer seasons with nRMSE equaling 24.31% and 8.41%, respectively. In addition, the daily comparison demonstrates that the RF model performs well for cloudy days with nRMSE = 41.40%, while the SVR model yields good results for sunny days with nRMSE = 8.88%. The results show that the daily classification technique enhances the forecasting accuracy of ML models. Furthermore, this study demonstrates that the forecasting accuracy of ML algorithms depends significantly on sky conditions.

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The design and operationalization of a wind energy system is mainly based on wind speed and wind direction, theses parameters depend on several geographic, temporal, and climatic factors. Fluctuating factors such as climate cause irregularities in wind energy production. Therefore, wind power forecasting is necessary before using wind power systems. Furthermore, in order to make informed decisions, it is necessary to explain the system's predictions to stakeholders. The explainable artificial intelligence (XAI) provides an interactive interface for intelligent systems to interact with machines, validate their results, and trust their behavior. In this paper, we provide an interpretable system for predicting wind energy using weather data. This system is based on a two-step method for fuzzy rules learning clustering (FRLC). The first step uses subtractive clustering and a linguistic approximation to extract linguistic rules. The second step uses linguistic hedges to refine linguistic rules. FRLC is compared to with artificial neural network (ANN), random forest (RF), k-nearest neighbors (K-NN), and support vector regression (SVR) models. The experimental results show that the accuracy of FRLC is acceptable regarding the comparison models and outperform them in terms of the interpretability. In parallel with prediction, FRLC model provides a set of linguistic fuzzy rules that explain the obtained results to the stakeholders.

The design and operationalization of a wind energy system is mainly based on wind speed and wind direction, theses parameters depend on several geographic, temporal, and climatic factors. Fluctuating factors such as climate cause irregularities in wind energy production. Therefore, wind power forecasting is necessary before using wind power systems. Furthermore, in order to make informed decisions, it is necessary to explain the system's predictions to stakeholders. The explainable artificial intelligence (XAI) provides an interactive interface for intelligent systems to interact with machines, validate their results, and trust their behavior. In this paper, we provide an interpretable system for predicting wind energy using weather data. This system is based on a two-step method for fuzzy rules learning clustering (FRLC). The first step uses subtractive clustering and a linguistic approximation to extract linguistic rules. The second step uses linguistic hedges to refine linguistic rules. FRLC is compared to with artificial neural network (ANN), random forest (RF), k-nearest neighbors (K-NN), and support vector regression (SVR) models. The experimental results show that the accuracy of FRLC is acceptable regarding the comparison models and outperform them in terms of the interpretability. In parallel with prediction, FRLC model provides a set of linguistic fuzzy rules that explain the obtained results to the stakeholders.