A Unique Unified Wind Speed Approach to Decision-Making for Dispersed Locations

Ayman M. Mansour, Abdulaziz Almutairi, Saeed Alyami, Mohammad A. Obeidat, Dhafer Almkahles and Jagabar Sathik
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Ayman M. Mansour: Department of Communication, Electronics and Computer Engineering, Tafila Technical University, Tafila 66110, Jordan
Abdulaziz Almutairi: Department of Electrical Engineering, College of Engineering, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Saeed Alyami: Department of Electrical Engineering, College of Engineering, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Mohammad A. Obeidat: Department of Electrical Power and Mechatronics Engineering, Tafila Technical University, Tafilah 66110, Jordan
Dhafer Almkahles: Renewable Energy Lab, Prince Sultan University, Riyadh 12435, Saudi Arabia
Jagabar Sathik: Renewable Energy Lab, Prince Sultan University, Riyadh 12435, Saudi Arabia

Sustainability, 2021, vol. 13, issue 16, 1-17

Abstract: The repercussions of high levels of environmental pollution coupled with the low reserves and increased costs of traditional energy sources have led to the widespread adaptation of wind energy worldwide. However, the expanded use of wind energy is accompanied by major challenges for electric grid operators due to the difficulty of controlling and forecasting the production of wind energy. The development of methods for addressing these problems has therefore attracted the interest of numerous researchers. This paper presents an innovative method for assessing wind speed in different and widely spaced locations. The new method uses wind speed data from multiple sites as a single package that preserves the characteristics of the correlations among those sites. Powerful Waikato Environment for Knowledge Analysis (Weka) machine learning software has been employed for supporting data preprocessing, clustering, classification, visualization, and feature selection and for using a standard algorithm to construct decision trees according to a training set. The resultant arrangement of the sites according to likely wind energy productivity facilitates enhanced decisions related to the potential for the effective operation of wind energy farms at the sites. The proposed method is anticipated to provide network operators with an understanding of the possible productivity of each site, thus facilitating their optimal management of network operations. The results are also expected to benefit investors interested in establishing profitable projects at those locations.

Keywords: data mining; decision tree; wind speed; renewable energy; system modeling; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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