Machine Learning for Prediction of Wind Power and Resources in Hot Climate Region

An assessment of wind speed and prediction of output power from a wind machine is important before implementing either commercial power plants or a standalone project. There are many studies targeting the improvement of wind power predictions and have achieved a desirable performance. This paper was conducted with the aim of obtaining accurate wind power production and leveraging the probability of reducing dependency on fossil fuels in the Kingdom of Saudi Arabia (KSA). The data of wind speed was collected from the Weather Online service (WO Service) to do the assessment for the city of Yanbu in KSA for two years. We have used four machine learning techniques in our proposed research to produce four models. Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), Super Vector Machine (SVM), and Gaussian Naive Bayes (GNB) were used within these models to evaluate the hourly wind speed and the wind turbine output power. The results of the four trained models show the estimated output power of 2 Megawatt (MW) from the wind. The CNN model showed the best performance since the mean absolute error was 0. 011, the test accuracy was 99.9886% and the predicted power was 7431.5 MW while the actual power is 7,432.28 MW.