EVALUATING THE PERFORMANCE AND ACCURACY OF AI TECHNIQUES IN WIND SPEED FORECASTING
DOI:
https://doi.org/10.29121/shodhkosh.v5.i7.2024.4335Keywords:
Wind Speed Forecasting, AI Techniques, Predictive Accuracy, Renewable Energy Management, XG Boost, Random ForestAbstract [English]
The effectiveness of many artificial intelligence systems in predicting wind speed is investigated in this work. Enhancing prediction accuracy and reliability for renewable energy management is the aim. Our analysis disproves the first theory that suggests complicated models perform better. Rather, we have shown that more straightforward methods, such as Random Forest regressors and XGBoost, consistently outperform their more complex equivalents. With a mean average percentage error of 6% and a prediction accuracy of almost 94%, these models exhibit remarkable precision, explaining roughly 91% of the variability in the data (R squared). Furthermore, they demonstrate computational efficiency, leading to faster processing times as compared to more complex models. Our study emphasises how important it is to carry out realistic model selection and empirical testing for wind speed predictions. This advances the strategies for managing renewable energy sources. This paper demonstrates the enhanced predictive capabilities of AI techniques, improving wind speed prediction accuracy and dependability. Thus, it facilitates making well-informed choices on the usage of renewable energy.
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Copyright (c) 2024 Dr. Ashish B. Sasankar, Priyanka Soitkar
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