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Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review.

Faisal Nawab^1,2, Ag Sufiyan Abd Hamid³, Adnan Ibrahim¹

¹Solar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.

|July 24, 2023

Summary

Accurate solar irradiation prediction is vital for solar energy and agriculture. Artificial intelligence (AI) methods, particularly artificial neural networks (ANNs), demonstrate superior accuracy over empirical models for solar irradiation forecasting.

Keywords:

ANFIS ANN Empirical SVM Solar irradiation

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Published on: December 12, 2013

Area of Science:

Renewable Energy Systems
Climate Science
Data Science

Background:

Solar irradiation data is crucial for the feasibility and effective utilization of solar energy projects, impacting both energy generation and agriculture.
Accurate solar irradiation prediction is essential for site selection, project sizing, and optimizing crop choices.
Physical measurement of solar irradiation is often impractical globally due to cost and technological limitations.

Purpose of the Study:

To conduct a comprehensive review and comparison of empirical and artificial intelligence (AI) techniques for solar irradiation prediction.
To identify the most accurate methods for forecasting solar irradiation based on recent scientific literature.

Main Methods:

Systematic literature review of research articles published between 2017 and 2022 focusing on solar irradiation prediction.
Comparison of the accuracy and applicability of empirical models (e.g., modified sunshine-based models) and AI methods (e.g., artificial neural networks, support vector machines).

Main Results:

AI methods generally exhibit higher accuracy in solar irradiation prediction compared to empirical methods.
Among empirical models, modified sunshine-based models (MSSM) showed the highest accuracy, followed by sunshine-based (SSM) and non-sunshine-based models (NSM).
Artificial neural networks (ANN) and hybrid models achieved the highest accuracy among AI techniques, with key inputs including temperature, humidity, clearness index, and precipitation.

Conclusions:

AI-based approaches, especially ANNs, are more effective for accurate solar irradiation forecasting.
Empirical models, particularly NSM, can be valuable when sunshine data is unavailable.
Simple empirical models predict accurately, and increasing polynomial order does not significantly improve results.