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This is a Quantitative Study study.

October 10, 2025Open Access

Forecasting Electicity in Photovoltaic Power Plants

Key Points

The ANN model achieved high predictive accuracy for forecasting electricity production from PV systems, ensuring reliable energy management.
The predictive model utilized meteorological inputs such as direct normal irradiation, diffuse radiation, and temperature data over a seven-year period.
Developed for rooftop photovoltaic power plants, the model supports effective integration of solar energy into existing power grids.
While the model was trained on historical data from 2017 to 2023, it indicates potential for future electricity forecasting under similar conditions.

Abstract

Abstract Due to the increasing global demand for sustainable energy and the variable nature of solar radiation, accurate forecasting of photovoltaic (PV) system performance has become essential. This study focuses on the development of an artificial neural network (ANN) model to predict monthly electricity production in a rooftop photovoltaic power plant. The model uses meteorological inputs such as direct normal irradiation, diffuse radiation, and average monthly temperature, covering the period from January 2017 to December 2023. The ANN demonstrated high predictive accuracy and reliability, making it a valuable tool for energy management, planning, and integration of PV systems into power grids. While the dataset spans 2017–2023, the model is structured to allow generalization for future forecasting applications under similar input conditions.

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Cite This Study

Ijačić et al. (Wed,) studied this question.

synapsesocial.com/papers/68e8619c7ef2f04ca37e4475 https://doi.org/https://doi.org/10.2478/bhee-2025-0009

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