ArticleScientific reports2024
Enhancing solar photovoltaic energy production prediction using diverse machine learning models tuned with the chimp optimization algorithm.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Assessment and pathways of the energy production revolution in the Yellow River Basin, China towards carbon peaking: a machine learning approach.Scientific reports · 2026Article
- Hybrid analysis of photovoltaic energy data containing excessive structural zeros.Scientific reports · 2026Article
- An interpretable statistical approach to photovoltaic power forecasting using factor analysis and ridge regression.Scientific reports · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared to predict energy production based on four independent weather variables: wind speed, relative humidity, ambient temperature, and solar irradiation. The evaluated models include multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), support vector regression (SVR), and multi-layer perceptron (MLP). These models were hyperparameter tuned using chimp optimization algorithm (ChOA) for a performance appraisal. The models are subsequently validated on the data from a 264 kWp PV system, installed at the Applied Science University (ASU) in Amman, Jordan. Of all 5 models, MLP shows best root mean square error (RMSE), with the corresponding value of 0.503, followed by mean absolute error (MAE) of 0.397 and a coefficient of determination (R
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