PREDICTING SOLAR POWER GENERATION: A MACHINE LEARNING APPROACH FOR GRID STABILITY AND EFFICIENCY

Popong Setiawati; Adhitio Satyo Bayangkari Karno; Widi Hastomo; Ellya Sestri; Dian Kasoni; Dodi Arif; Fahrul Razi

doi:10.33480/pilar.v21i1.6126

Authors

Popong Setiawati Esa Unggul University
Adhitio Satyo Bayangkari Karno Gunadarma University
Widi Hastomo ITB Ahmad Dahlan
Ellya Sestri ITB Ahmad Dahlan
Dian Kasoni STMIK Antar Bangsa
Dodi Arif Gunadarma University
Fahrul Razi ITB Ahmad Dahlan

DOI:

https://doi.org/10.33480/pilar.v21i1.6126

Keywords:

energy forecasting, machine learning, renewable energy

Abstract

In countries with high levels of insolation, the demand for renewable energy sources has driven the rapid emergence and growth of solar power plants. Maintaining grid stability and efficient power management in response to weather variations that affect solar radiation intensity and battery consumption limits remains a major challenge. This study aims to develop a machine learning-based prediction model to estimate the electricity generated by solar power plants using weather data. Four algorithms are utilized: Linear Regression, Random Forest Regressor, Decision Tree Regressor, and Gradient Boosting Regressor. The results show that the Random Forest algorithm produces the best model, with MAE and RMSE values of 0.1114281 and 0.3187232, respectively. This research contributes to the literature, particularly on the relatively unexplored topic of using multiple machine learning models to predict energy output from photovoltaic systems. The findings have the potential to inform more efficient energy policies and improve energy integration technologies for grid-connected solar power systems.

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