What does this research mean for the field?

The Random Forest model provides the most accurate prediction of photovoltaic system conversion efficiency among evaluated machine learning models, demonstrating its practical utility despite slightly higher training costs. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Evaluate and compare six data-driven machine learning algorithms for predicting the combined module and inverter conversion efficiency of an on-grid rooftop photovoltaic system.

August 22, 2026SolarOpen Access

Data-Driven Prediction of Photovoltaic System Efficiency: A Case Study of a Rooftop System in Jordan

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Authors

BHBashar Hammad SASameer Al‐Dahidi MAMohammad Al−Abed

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Overview

Comparative modeling study finds Random Forest achieves superior accuracy for solar conversion efficiency, highlighting its utility for grid-connected photovoltaic system management.

Key Points

Evaluate and compare six data-driven machine learning algorithms for predicting the combined module and inverter conversion efficiency of an on-grid rooftop photovoltaic system.
Analyzed 179 daily operational samples collected from a 7.98 kWp rooftop on-grid photovoltaic system in Jordan between March 17 and September 24, 2014.
Evaluated six machine learning models (Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, Gaussian Process Regression, and Elastic Net) and benchmarked them against six previously reported models using MSE, accuracy, R², adjusted R², and an unweighted Decision-Making Matrix.
Random Forest demonstrated the best overall predictive performance and consistently lowest prediction error among all 12 proposed and benchmarked models.
Support Vector Machine, Decision Tree, and Gradient Boosting showed moderate predictive capability, while Elastic Net was the worst-performing model overall.

Cite This Study

Hammad et al. (2026) studied this question.

synapsesocial.com/papers/6a895f62ca7ade938187e025 https://doi.org/10.3390/solar6040052

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August 22, 2026SolarOpen Access

Data-Driven Prediction of Photovoltaic System Efficiency: A Case Study of a Rooftop System in Jordan

View Full Paper

Ask AI

Bookmark

Authors

BHBashar Hammad SASameer Al‐Dahidi MAMohammad Al−Abed

Discussion

Member takes

Overview

Comparative modeling study finds Random Forest achieves superior accuracy for solar conversion efficiency, highlighting its utility for grid-connected photovoltaic system management.

Key Points

Evaluate and compare six data-driven machine learning algorithms for predicting the combined module and inverter conversion efficiency of an on-grid rooftop photovoltaic system.
Analyzed 179 daily operational samples collected from a 7.98 kWp rooftop on-grid photovoltaic system in Jordan between March 17 and September 24, 2014.
Evaluated six machine learning models (Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, Gaussian Process Regression, and Elastic Net) and benchmarked them against six previously reported models using MSE, accuracy, R², adjusted R², and an unweighted Decision-Making Matrix.
Random Forest demonstrated the best overall predictive performance and consistently lowest prediction error among all 12 proposed and benchmarked models.
Support Vector Machine, Decision Tree, and Gradient Boosting showed moderate predictive capability, while Elastic Net was the worst-performing model overall.

Cite This Study

Hammad et al. (2026) studied this question.

synapsesocial.com/papers/6a895f62ca7ade938187e025 https://doi.org/10.3390/solar6040052

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