Nowcasting Hourly-Averaged Tilt Angles of Acceptance for Solar Collector Applications Using Machine Learning Models

Ronewa Collen Nemalili (), Lordwell Jhamba, Joseph Kiprono Kirui and Caston Sigauke
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Ronewa Collen Nemalili: Department of Physics, University of Venda, Thohoyandou 0950, South Africa
Lordwell Jhamba: Department of Physics, University of Venda, Thohoyandou 0950, South Africa
Joseph Kiprono Kirui: Department of Physics, University of Venda, Thohoyandou 0950, South Africa
Caston Sigauke: Department of Physics, University of Venda, Thohoyandou 0950, South Africa

Energies, 2023, vol. 16, issue 2, 1-19

Abstract: Challenges in utilising fossil fuels for generating energy call for the adoption of renewable energy sources. This study focuses on modelling and nowcasting optimal tilt angle(s) of solar energy harnessing using historical time series data collected from one of South Africa’s radiometric stations, USAid Venda station in Limpopo Province. In the study, we compared random forest (RF), K-nearest neighbours (KNN), and long short-term memory (LSTM) in nowcasting of optimum tilt angle. Gradient boosting (GB) is used as the benchmark model to compare the model’s predictive accuracy. The performance measures of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R 2 were used, and the results showed LSTM to have the best performance in nowcasting optimum tilt angle compared to other models, followed by the RF and GB, whereas KNN was the worst-performing model.

Keywords: renewable energy; machine learning; tilt angle; solar irradiance; global horizontal irradiance; gradient boosting; LSTM; KNN; random forest; nowcasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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