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Research Papers: Alternative Energy Sources

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
e-mail: dina.fawzy@cis.asu.edu.eg

Sherin Moussa

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

Nagwa Badr

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

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received August 8, 2017; final manuscript received October 2, 2017; published online November 9, 2017. Assoc. Editor: Ryo Amano.

J. Energy Resour. Technol 140(5), 051202 (Nov 09, 2017) (13 pages) Paper No: JERT-17-1422; doi: 10.1115/1.4038119 History: Received August 08, 2017; Revised October 02, 2017

A fast-growing worldwide interest is directed toward 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 traditional approaches. In this paper, we propose Trio-V Wind Analyzer (WA) 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 multicriteria evaluation (MCE). It utilizes principal component analysis (PCA) and our proposed Double-Reduction Optimum Apriori (DROA) to analyze most of the environmental, physical, and economical criteria. In addition, Trio-V WA 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 WA 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.

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Topics: Turbines , Wind , Wind farms , Design
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Figures

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Fig. 1

Trio-V WA architecture

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Fig. 2

Trio-V WA database schema

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Fig. 3

A sample for the cells static data at Trio-V WA

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Fig. 4

A sample for the cells dynamic data at Trio-V WA

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Fig. 5

Trio-V WA system main flowchart

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Fig. 6

Trio-V WA system main user interface

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Fig. 7

The suitability analysis results plotted on the map

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Fig. 8

Processing time versus reduction methods on suitability degree experiments

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Fig. 9

Suitability degree results versus reduction methods

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Fig. 10

Trio-V WA suitability degree evaluation accuracy

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Fig. 11

Trio-V WA power prediction accuracy

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