Version 1
: Received: 7 April 2024 / Approved: 8 April 2024 / Online: 8 April 2024 (11:22:28 CEST)
How to cite:
Mugware, F.W.; Sigauke, C.; Ravele, T. Assessing the Predictive Power of Machine Learning Models for Wind Speed Prediction under Different Weather Conditions. Preprints2024, 2024040525. https://doi.org/10.20944/preprints202404.0525.v1
Mugware, F.W.; Sigauke, C.; Ravele, T. Assessing the Predictive Power of Machine Learning Models for Wind Speed Prediction under Different Weather Conditions. Preprints 2024, 2024040525. https://doi.org/10.20944/preprints202404.0525.v1
Mugware, F.W.; Sigauke, C.; Ravele, T. Assessing the Predictive Power of Machine Learning Models for Wind Speed Prediction under Different Weather Conditions. Preprints2024, 2024040525. https://doi.org/10.20944/preprints202404.0525.v1
APA Style
Mugware, F.W., Sigauke, C., & Ravele, T. (2024). Assessing the Predictive Power of Machine Learning Models for Wind Speed Prediction under Different Weather Conditions. Preprints. https://doi.org/10.20944/preprints202404.0525.v1
Chicago/Turabian Style
Mugware, F.W., Caston Sigauke and Thakhani Ravele. 2024 "Assessing the Predictive Power of Machine Learning Models for Wind Speed Prediction under Different Weather Conditions" Preprints. https://doi.org/10.20944/preprints202404.0525.v1
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 artificial neural networks (ANN) and convolutional neural networks (CNN) in predicting wind speed across three locations in South Africa, characterised by different weather patterns. Empirical results show that CNN outperforms ANN 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. 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; ANN; CNN
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
