Machine Learning Approach to Wind Speed Prediction using Soft Computing Tools

Anas Faskari Shehu, Talatu Asi'au Belgore

Abstract


Despite the widespread use of mathematical models in forecasting climatic processes, limited research has been conducted to compare these models. This lack of comparison is particularly significant due to the unpredictable nature of natural wind, which leads to fluctuations in wind power and challenges the stability of wind power systems, hindering the large-scale integration of wind power into the grid. To address this issue, this study proposes a novel wind speed prediction scheme, recognizing the critical role of wind speed prediction in ensuring the safety and stability of wind energy production systems. The research introduces a method that utilizes machine learning models to predict wind speed and optimize the design of energy systems. Two machine learning algorithms are employed in this study: the neural predictor of the Artificial Neural Network, and the classification regression technique, which is especially effective when the number of dimensions exceeds the number of samples, as is often the case with support vector machines (SVM). The dataset used for this research consists of measured and satellite data obtained from the Nigeria Meteorological Agency (NiMet) for the year 2016. The proposed combination forecasting model in this paper demonstrates a well-defined structure and provides reliable predictions. The outcomes presented in this study will advance the field of wind energy prediction, support wind farms in developing wind power control systems, and contribute to the development of sustainable green energy infrastructure.


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References


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