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
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Accurate prediction of PV system power output is necessary to enhance the integration of renewable energy into the grid. The study focuses on utilizing machine learning (ML) methodologies
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By investigating the most recent literature, this review identifies critical research gaps and suggests future directions for enhancing forecasting models, including improving model
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From the foregoing discussions on solar power generation model developments, this study develops a differential solar power generation model for the simulation of solar power...
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Energy production estimates generated by developers and independent engineering firms are a critical part of the package reviewed by investors.
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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
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In this paper, we propose a Bayesian approach to estimate the curve of a function f ( ) that models the solar power generated at k moments per day for n days and to forecast the curve for
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This research uses deep learning techniques, the Long Short-Term memory (LSTM) model, to predict solar power generation from several environmental variables, including solar
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The objectives of the proposed research include the development of a robust and scalable model for accurate solar power prediction using state-of-the-art DL techniques.
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Hence, this study proposes the Extreme Gradient Boosting regression-based Solar Photovoltaic Power Generation Prediction (XGB-SPPGP) model to predict and classify the usage of
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