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Article

Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

Energy Research Lab, College of Engineering, Effat University, P.O. Box 34689, Jeddah 21478, Saudi Arabia
Energies 2019, 12(24), 4669; https://doi.org/10.3390/en12244669
Received: 28 October 2019 / Revised: 26 November 2019 / Accepted: 3 December 2019 / Published: 9 December 2019
(This article belongs to the Section Electrical Power and Energy System)
Predicting wind speed for wind energy conversion systems (WECS) is an essential monitor, control, plan, and dispatch generated power and meets customer needs. The Kingdom of Saudi Arabia recently set ambitious targets in its national transformation program and Vision 2030 to move away from oil dependence and redirect oil and gas exploration efforts to other higher-value uses, chiefly meeting 10% of its energy demand through renewable energy sources. In this paper, we propose the use of the artificial neural networks (ANNs) method as a means of predicting daily wind speed in a number of locations in the Kingdom of Saudi Arabia based on multiple local meteorological measurement data provided by K.A.CARE. The suggested model is a feed-forward neural network model with the administered learning technique using a back-propagation algorithm. Results indicate that the best structure is obtained with thirty neurons in the hidden layers matching a minimum root mean square error (RMSE) and the highest correlation coefficient (R). A comparison between predicted and actual data from meteorological stations showed good agreement. A comparison between five machine learning algorithms, namely ANN, support vector machines (SVM), random tree, random forest, and RepTree revealed that random tree has low correlation and relatively high root mean square error. The significance of the present study relies on its ability to predict wind speeds, a necessary prerequisite to executing sustainable integration of wind power into Saudi Arabia’s electrical grid, assisting operators in efficiently managing generated power, and helping achieve the energy efficiency and production targets of Vision 2030. View Full-Text
Keywords: wind turbines; wind speed; machine learning; wind energy conversion systems wind turbines; wind speed; machine learning; wind energy conversion systems
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MDPI and ACS Style

Brahimi, T. Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia. Energies 2019, 12, 4669. https://doi.org/10.3390/en12244669

AMA Style

Brahimi T. Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia. Energies. 2019; 12(24):4669. https://doi.org/10.3390/en12244669

Chicago/Turabian Style

Brahimi, Tayeb. 2019. "Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia" Energies 12, no. 24: 4669. https://doi.org/10.3390/en12244669

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