Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model

Deo, Ravinesh C. and Ghimire, Sujan and Downs, Nathan J. and Raj, Nawin (2018) Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model. In: Handbook of research on predictive modeling and optimization methods in science and engineering. Advances in Computational Intelligence and Robotics (ACIR) Book Series. IGI Publishing (IGI Global), Hershey, United States, pp. 328-359. ISBN 9781522547662

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The precise prediction of windspeed is essential in order to improve and optimize wind power prediction. However, due to the sporadic and inherent complexity of weather parameters, the prediction of windspeed data using different patterns is difficult. Machine learning (ML) is a powerful tool to deal with uncertainty and has been widely discussed and applied in renewable energy forecasting. In this chapter, the authors present and compare an artificial neural network (ANN) and genetic programming (GP) model as a tool to predict windspeed of 15 locations in Queensland, Australia. After performing feature selection using neighborhood component analysis (NCA) from 11 different metrological parameters, seven of the most important predictor variables were chosen for 85 Queensland locations, 60 of which were used for training the model, 10 locations for model validation, and 15 locations for the model testing. For all 15 target sites, the testing performance of ANN was significantly superior to the GP model.

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Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published chapter deposited in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 07 Aug 2018 05:31
Last Modified: 02 Oct 2018 03:52
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.4018/978-1-5225-4766-2.ch015
URI: http://eprints.usq.edu.au/id/eprint/34659

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