Work

Evaluation of Machine Learning Models in Solar Radiation Prediction for Photovoltaic System Design

Year: 2025

Type: article

Abstract: This research evaluates machine learning models in predicting solar radiation, crucial for designing photovoltaic systems. Accuracy in solar forecasting is key to mitigating climate change and meeting energy demand. Advanced machine learning techniques were applied, surpassing traditional models in precision and efficiency, including SARIMA, Random Forests, SVM, ANN, and LSTM, assessed with metrics such as accuracy, sensitivity, precision, NME, R2, and execution time. After normalization, the SVM model achieved the highest overall score of 5.86. A photovoltaic system was sized using an SVM model with solar radiation data (2017-2020). Predictions calculated an average daily consumption of 4.89 kWh, a total daily energy of 109.88 kWh, and a solar panel area of 4.42 m2. The system’s peak power is 0.86 kWp, and the inverter power with a safety margin is 1.04 kW.

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/e3sconf/202568000010/pdf

Authors: Roberto Salazar-Achig, Alex Paguay, Jorge Gavilanes, Elizabeth Salazar-Jácome, Yonatan Aguirre

Institutions: Universidad Técnica de Cotopaxi, Universidad de las Fuerzas Armadas ESPE, Universidad Israel, Universidad Nacional de Piura

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7116704433

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