Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model
Edited book (chapter)
Chapter Title | Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model |
---|---|
Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 2177 |
Book Title | Handbook of research on predictive modeling and optimization methods in science and engineering |
Authors | Deo, Ravinesh C. (Author), Ghimire, Sujan (Author), Downs, Nathan J. (Author) and Raj, Nawin (Author) |
Editors | Kim, Dookie, Roy, Sanjiban Sekhar, Lansivaara, Tim, Deo, Ravinesh C. and Samui, Pijush |
Page Range | 328-359 |
Series | Advances in Computational Intelligence and Robotics (ACIR) Book Series |
Chapter Number | 15 |
Number of Pages | 32 |
Year | 2018 |
Publisher | IGI Global |
Place of Publication | Hershey, United States |
ISBN | 9781522547662 |
9781522547679 | |
ISSN | 2327-0411 |
2327-042X | |
Digital Object Identifier (DOI) | https://doi.org/10.4018/978-1-5225-4766-2.ch015 |
Web Address (URL) | https://www.igi-global.com/book/handbook-research-predictive-modeling-optimization/185480 |
Abstract | 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. |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q4x19/optimization-of-windspeed-prediction-using-an-artificial-neural-network-compared-with-a-genetic-programming-model
Download files
454
total views332
total downloads0
views this month0
downloads this month