Publication: Wind speed modeling over complex terrain with the artificial neural network in the measure-correlate-predict technique: A case study of Malaysia
Date
2022
Authors
Kim Hwang Y.
Zamri Ibrahim M.
Ismail M.
Najah Ahmed A.
Albani A.
Journal Title
Journal ISSN
Volume Title
Publisher
SAGE Publications Inc.
Abstract
This study aimed to create a Malaysian wind map of greater accuracy. Compared to a previous wind map, spatial modeling input was increased. The Genetic Algorithm-optimized Artificial Neural Network Measure�Correlate�Predict method was used to impute missing data, and managed to control over- or under-prediction issues. The established wind map was made more reliable by including surface roughness to simulate wind flow over complex terrain. Validation revealed that the current wind map is 33.833% more accurate than the previous wind map. Furthermore, the correlation coefficient between wind map-simulated data and observed data was high as 0.835. In conclusion, the new and improved wind map for Malaysia simulates data with acceptable accuracy. � The Author(s) 2021.
Description
Complex networks; Forecasting; Genetic algorithms; Neural networks; Wind; Wind power; Case-studies; Complex terrains; Malaysia; Malaysians; MCP; Measure-correlate-predict; Model inputs; Spatial modelling; Wind maps; Wind speed models; Surface roughness; alternative energy; artificial neural network; genetic algorithm; renewable resource; roughness; wind power; Malaysia