Machine learning techniques for predicting the production capacity of a windfarm based on daily wind speed data

Among renewable energy sources, wind energy is a substantial and suitable source with the capacity to provide electricity continuously and sustainably. However, wind energy has some obstacles, including high initial investment prices, the fixed nature of wind turbines, and the difficulty in locating wind-efficient energy zones. Long-term wind power forecasting was accomplished in this work by utilizing two machine learning algorithms based on daily wind speed data. We suggested a system for forecasting wind power values based on machine learning algorithms. The findings indicated that machine learning techniques might be used to anticipate long-term wind power values based on past wind speed data. Furthermore, the results demonstrated that machine learning-based models could be applied to not model-trained sites. This research revealed that machine learning techniques might be effectively used before constructing wind turbines in an unknown geographical region

Machine learning techniques for predicting the production capacity of a windfarm based on daily wind speed data Keywords:

Renewable energy , Wind energy , Machine learning algorithms , Wind power forecasting