Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions

Fhulufhelo Walter Mugware, Caston Sigauke () and Thakhani Ravele
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Fhulufhelo Walter Mugware: Department of Mathematical and Computational Sciences, University of Venda, Private Bag X5050, Thohoyandou 0950, Limpopo, South Africa
Caston Sigauke: Department of Mathematical and Computational Sciences, University of Venda, Private Bag X5050, Thohoyandou 0950, Limpopo, South Africa
Thakhani Ravele: Department of Mathematical and Computational Sciences, University of Venda, Private Bag X5050, Thohoyandou 0950, Limpopo, South Africa

Forecasting, 2024, vol. 6, issue 3, 1-28

Abstract: The main source of electricity worldwide stems from fossil fuels, contributing to air pollution, global warming, and associated adverse effects. This study explores wind energy as a potential alternative. Nevertheless, the variable nature of wind introduces uncertainty in its reliability. Thus, it is necessary to identify an appropriate machine learning model capable of reliably forecasting wind speed under various environmental conditions. This research compares the effectiveness of Dynamic Architecture for Artificial Neural Networks (DAN2), convolutional neural networks (CNN), random forest and XGBOOST in predicting wind speed across three locations in South Africa, characterised by different weather patterns. The forecasts from the four models were then combined using quantile regression averaging models, generalised additive quantile regression (GAQR) and quantile regression neural networks (QRNN). Empirical results show that CNN outperforms DAN2 in accurately forecasting wind speed under different weather conditions. This superiority is likely due to the inherent architectural attributes of CNNs, including feature extraction capabilities, spatial hierarchy learning, and resilience to spatial variability. The results from the combined forecasts were comparable with those from the QRNN, which was slightly better than those from the GAQR model. However, the combined forecasts were more accurate than the individual models. These results could be useful to decision-makers in the energy sector.

Keywords: air pollution; global warming; fossil fuels; renewable energy; carbon emissions; volatility; reliability; machine learning; DAN2; CNN (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2024
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