Predicting Solar Irradiance at Several Time Horizons Using Machine Learning Algorithms

Chibuzor N. Obiora (), Ali N. Hasan and Ahmed Ali
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Chibuzor N. Obiora: Department of Electrical and Electronic Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa
Ali N. Hasan: Department of Electrical and Electronic Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa
Ahmed Ali: Department of Electrical and Electronic Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa

Sustainability, 2023, vol. 15, issue 11, 1-17

Abstract: Photovoltaic (PV) panels need to be exposed to sufficient solar radiation to produce the desired amount of electrical power. However, due to the stochastic nature of solar irradiance, smooth solar energy harvesting for power generation is challenging. Most of the available literature uses machine learning models trained with data gathered over a single time horizon from a location to forecast solar radiation. This study uses eight machine learning models trained with data gathered at various time horizons over two years in Limpopo, South Africa, to forecast solar irradiance. The goal was to study how the time intervals for forecasting the patterns of solar radiation affect the performance of the models in addition to determining their accuracy. The results of the experiments generally demonstrate that the models’ accuracy decreases as the prediction horizons get longer. Predictions were made at 5, 10, 15, 30, and 60 min intervals. In general, the deep learning models outperformed the conventional machine learning models. The Convolutional Long Short-Term Memory (ConvLSTM) model achieved the best Root Mean Square Error (RMSE) of 7.43 at a 5 min interval. The Multilayer Perceptron (MLP) model, however, outperformed other models in most of the prediction intervals.

Keywords: deep learning; machine learning; solar irradiance; prediction; algorithms; time horizons (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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