This study proposes the Extreme Gradient Boosting-based Solar Photovoltaic Power Generation Prediction (XGB-SPPGP) model to predict solar irradiance and power with minimal error.
By analyzing power generation data and employing advanced ML models, the research aims to enhance the efficiency and predictability of solar energy systems. The significance of this
An overview of the implicit SPGMBCT model is vital in the development of the power generation model since it is found in the linear correlation model, which describes solar power...
Estimates the energy production and cost of energy of grid-connected photovoltaic (PV) energy systems throughout the world. It allows homeowners, small building owners, installers and manufacturers to
Therefore, precise solar power generation forecasting is necessary for a renewable energy system to operate effectively and economically. In this study, various machine learning
This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar
Radiation, sunshine, and air temperature emerged as significant predictors of solar power generation. The Random Forest Regressor was identified as the best-performing model, achieving an R-squared
The development of a solar power generation model, multiple differential models, simulation and experimentation with a pilot solar rig served as alternate model for the prediction of
Solar power generation in smart cities encompasses a wide array of applications, ranging from rooftop solar panels on residential buildings to expansive solar farms integrated into urban
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