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An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia

The major issues on the wind measurement campaign are the data measured in a short period and the occurrence of missing data due to the failure of the measurement instrument. Meanwhile, Measure-Correlate-Predict (MCP) method had widely been used to predict the long-term condition and missing data at...

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Main Authors: Hwang, Y.K., Ibrahim, M.Z., Ahmed, A.N., Albani, A.
Format: Conference Paper
Language:English
Published: 2020
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spelling my.uniten.dspace-131502020-07-06T03:06:13Z An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia Hwang, Y.K. Ibrahim, M.Z. Ahmed, A.N. Albani, A. The major issues on the wind measurement campaign are the data measured in a short period and the occurrence of missing data due to the failure of the measurement instrument. Meanwhile, Measure-Correlate-Predict (MCP) method had widely been used to predict the long-term condition and missing data at the measurement site based on nearest Malaysian Meteorological Department (MMD), Meteorological Aerodrome Report (METAR) and extended Climate Forecast System Reanalysis (ECFSR) data. In this research, the long-term wind data at selected potential sites in Malaysia were predicted by optimized Artificial Neural Networks (ANNs). The Genetic Algorithm (GA) was applied to optimize the ANN. Five different ANN MCP models had been designed based on different types of reference data and different temporal scales to predict wind data at three target sites. Weibull frequency distributions and RMSE examined predicted wind data. The prediction of ANN had been improved in between 20.562% to 113.573% by GA optimization. The best R-value obtained from optimization were affected the Weibull shape and scale of predicted data. At last, the result revealed that the optimized ANN model could predict the long-term data for the target site with better accuracy. © 2018 Asian Institute of Technology. 2020-02-03T03:30:44Z 2020-02-03T03:30:44Z 2019 Conference Paper 10.23919/ICUE-GESD.2018.8635790 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description The major issues on the wind measurement campaign are the data measured in a short period and the occurrence of missing data due to the failure of the measurement instrument. Meanwhile, Measure-Correlate-Predict (MCP) method had widely been used to predict the long-term condition and missing data at the measurement site based on nearest Malaysian Meteorological Department (MMD), Meteorological Aerodrome Report (METAR) and extended Climate Forecast System Reanalysis (ECFSR) data. In this research, the long-term wind data at selected potential sites in Malaysia were predicted by optimized Artificial Neural Networks (ANNs). The Genetic Algorithm (GA) was applied to optimize the ANN. Five different ANN MCP models had been designed based on different types of reference data and different temporal scales to predict wind data at three target sites. Weibull frequency distributions and RMSE examined predicted wind data. The prediction of ANN had been improved in between 20.562% to 113.573% by GA optimization. The best R-value obtained from optimization were affected the Weibull shape and scale of predicted data. At last, the result revealed that the optimized ANN model could predict the long-term data for the target site with better accuracy. © 2018 Asian Institute of Technology.
format Conference Paper
author Hwang, Y.K.
Ibrahim, M.Z.
Ahmed, A.N.
Albani, A.
spellingShingle Hwang, Y.K.
Ibrahim, M.Z.
Ahmed, A.N.
Albani, A.
An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia
author_facet Hwang, Y.K.
Ibrahim, M.Z.
Ahmed, A.N.
Albani, A.
author_sort Hwang, Y.K.
title An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia
title_short An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia
title_full An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia
title_fullStr An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia
title_full_unstemmed An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia
title_sort optimized ann measure-correlate-predict method for long-term wind prediction in malaysia
publishDate 2020
_version_ 1672614210467528704
score 13.222552

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