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research-article

Trio-V Wind Analyzer: A Generic Integral System for Wind Farm Suitability Design and Power Prediction Using Big Data Analytics

[+] Author and Article Information
Dina Fawzy

Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
dina.fawzy@cis.asu.edu.eg

Sherin Moussa

Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
sherinmoussa@cis.asu.edu.eg

Nagwa Badr

Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
nagwabadr@cis.asu.edu.eg

1Corresponding author.

ASME doi:10.1115/1.4038119 History: Received August 08, 2017; Revised October 02, 2017

Abstract

A fast-growing worldwide interest is directed towards green energies. Due to the huge costs of wind farms establishment, the location for wind farms should be carefully determined to achieve the optimum return of investment. Consequently, researches have been conducted to investigate land suitability prior to wind plants development. The generated data from the sensors detecting a potential land can be very huge, fast in generation, heterogeneous, and incomplete, which become seriously difficult to process using traditionalapproaches. In this paper, we propose Trio-V Wind Analyzer that handles data volume, variety, and veracity to identify the most suitable location for wind energy development in any study area using a modified version of Multi-Criteria Evaluation. It utilizes Principle Component Analysis and our proposed DoubleReduction Optimum Apriori to analyze most of the environmental, physical, and economical criteria. In addition, Trio-V Wind Analyzer recommends the suitable turbines and proposes the adequate turbines' layout distribution, predicting the expected power generated based on the recommended turbine's specifications using a regression technique. Thus, Trio-V Wind Analyzer provides an integral system of land evaluation for potential investment in wind farms. Experiments indicate 80% and 95% average accuracy for land suitability degree and power prediction respectively with efficient performance.

Copyright (c) 2017 by ASME
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