September 5, 2025Journal of Science and TechnologyOpen Access

Improving the Accuracy of Solar Energy Production Forecasting in Libya Using Advanced Linear

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The study achieved a Mean Absolute Error (MAE) of 20.44 kWh for solar energy predictions.
Data was collected from the NASA POWER platform, focusing on solar radiation, temperature, and humidity in Libya.
The linear regression model's limitations became evident during unstable weather conditions, underscoring future integration of neural networks.
These findings suggest significant implications for Libya's energy institutions in solar grid planning and cost reduction.

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Implication

Research reveals improvements in forecasting solar energy production using linear regression in Libya, suggesting advanced methods may enhance accuracy.

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garash et al. (2025) studied this question.

synapsesocial.com/papers/68c23a2cb210217d6477ef6e https://doi.org/10.20428/jst.v30i8.2886

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Implication

Research reveals improvements in forecasting solar energy production using linear regression in Libya, suggesting advanced methods may enhance accuracy.

Improving the Accuracy of Solar Energy Production Forecasting in Libya Using Advanced Linear

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Discussion

Implication

Cite This Study

Improving the Accuracy of Solar Energy Production Forecasting in Libya Using Advanced Linear

Key Points

Discussion

Implication

Cite This Study

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