Investigation of Data Size Variability in Wind Speed Prediction Using AI Algorithms

Area of Science:

• Renewable Energy Systems
• Artificial Intelligence Applications
• Environmental Science

Background:

• Fossil fuel energy generation significantly contributes to global warming.
• Renewable energy offers a sustainable alternative, reducing industrial emissions.
• Decentralized energy production enhances energy independence.

Purpose of the Study:

• To predict wind speed for improved wind farm planning and feasibility.
• To evaluate the accuracy of various artificial intelligence algorithms for wind speed prediction.

Main Methods:

• Collected meteorological data was utilized.
• Twelve distinct artificial intelligence algorithms were employed for wind speed prediction.
• Model performance was assessed based on prediction accuracy across different dataset sizes.

Main Results:

• The study compared the performance of twelve artificial intelligence algorithms.
• Wind speed prediction accuracy varied among the algorithms.
• The optimal algorithm for wind speed prediction was found to be data-size dependent.

Conclusions:

• Accurate wind speed prediction is essential for effective wind energy integration.
• Artificial intelligence offers powerful tools for forecasting intermittent renewable resources like wind.
• Selecting the appropriate AI model based on data availability is key to successful wind farm development.