Wind speed modeling over complex terrain with the artificial neural network in the measure-correlate-predict technique: A case study of Malaysia. Issue 3 (June 2022)

• Record Type: Journal Article
• Authors: Kim Hwang, Yong; Zamri Ibrahim, Mohd; Ismail, Marzuki; Najah Ahmed, Ali; Albani, Aliashim
• Is Part Of: Wind engineering. Volume 46:Issue 3(2022)
• Page Start: 818
• Page End: 843
• Publication Date: 2022-06
• Subjects: Wind -- wind energy -- spatial modeling -- MCP -- surface roughness; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X211055836
• Languages: English
• ISSNs: 0309-524X

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.