This study utilizes six years of meteorological data from Duzce to predict solar energy output using 14 machine learning (ML) algorithms and contributes to artificial intelligence by advancing ML-based forecasting methods and supports engineering practices by optimizing PV system management.
The results show that reliable and interpretable photovoltaic prediction can be achieved from very limited field measurements when model selection and validation are carefully designed under data-scarce operating conditions.
Accurate prediction of photovoltaic (PV) power is essential for effective monitoring, control, and grid integration of large-scale solar systems. This study uses high-resolution data from a 20 MW utility-scale PV plant to benchmark four advanced machine learning (ML) models: Support Vector Machine (SVM), Random Forest...
Ahmed Saidi, Abdelghani Draoui, T. Abdelouahed· Informatyka Automatyka Pomia...· 0 citations
The transition toward renewable energy integration in power systems presents challenges in managing photovoltaic (PV) power output owing to variations in operating conditions. This study evaluated the performance of K-Nearest Neighbor (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP), with and wi...
Ilham Maridi, Fiky Anggara, Martati Martati· Jurnal Media Elektrik· 0 citations
This study demonstrates the effectiveness of gradient boosting models and neural networks for accurately forecasting solar production in Meknes, thus offering promising prospects for the deployment of solar solutions in Morocco.
Nasyra Elouastani, M. Moussaoui, S. Amraqui· EPJ Web of Conferences· 0 citations
Accurate prediction of the Performance Ratio (PR) of photovoltaic (PV) systems in complex mountainous terrain is critical for bankable project planning. However, fixed-PR assumptions systematically underestimate the influence of local topography and tropical microclimates. This study integrates drone-captured micro-top...
Nur Qudus, Rizky Ajie Aprilianto, Nur Anita et al.· E3S Web of Conferences· 0 citations
The integration of sustainable energy into atmospheric water harvesting offers a promising solution for supplementary water supply in off-grid and dry regions. This study investigates the performance of a hybrid photovoltaic–atmospheric water generator (PV-AWG) system and evaluates machine learning (ML) models for pred...
Ahmad Rizal Sultan, Maya Itasari, Muhdalifah Muhtar et al.· Bulletin of Electrical Engin...· 0 citations
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