Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids

Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids

In the current technological era, predicting the power and energy output based on the changing weather factors play an important role in the economic growth of the renewable energy sector. Unlike traditional fossil fuel-based resources, renewable energy sources potentially play a pivotal role in sustaining a country's economy and improving the quality of life. As our planet is nowadays facing serious challenges due to climate change and global warming, this research could be effective to achieve good prediction accuracy in smart grids using different weather conditions. In the current study, different machine learning models are compared to estimate power and energy of hybrid photovoltaic (PV)-wind renewable energy systems using seven weather factors that have a significant impact on the output of the PV–wind renewable energy system. This study classified the machine learning model which could be potentially useful and efficient to predict energy and power. The historic hourly data is processed with and without data manipulation. While data manipulations are carried out using recursive feature elimination using cross-validation (RFECV). The data is trained using artificial neural network (ANN) regressors and correlations between different features within the dataset are identified. The main aim is to find meaningful patterns that could help statistical learning models train themselves based on these usage patterns. The results suggest that opting feature selection technique using linear regression model outperforms all the other models in all evaluation metrics having to mean squared error (MSE) of 0.000000104, mean absolute error (MAE) of 0.00083, R2 of 99.6%, and computation time of 0.02 s The results investigated depict that the sustainable computational scheme introduced has vast potential to enhance smart grids efficiency by predicting the energy produced by renewable energy systems.