Wind resource forecasting using enhanced measure correlate predict (MCP)

As'Ad Zakaria, Wolf-Gerrit Fruh, Firas Basim Ismail

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

The enhancement of Measure Correlate Predict (MCP) using Principal Component Analysis (PCA) is a new wind prediction method based on studying the patterns of historical wind data. The method is trained based on past wind data to predict the wind speed using an ensemble of similar past events. The method is tested based on Meteorological Office (MET-Office) wind speed from a reference site that spans from 2000 to 2010. The last two years (2009 to 2010) were used as training years where the MCP - PCA algorithm learns the wind patterns between the reference(s) and target(s) site. The prediction result is then compared to the actual wind speed distribution at the target site of the training years. The method is further tested with an increase in number of reference sites for predictions. The new prediction results show that the prediction error improves to 23.1 % in average in comparison to a standard linear regression method.

Original languageEnglish
Title of host publication6th International Conference on Production, Energy and Reliability 2018
Subtitle of host publicationWorld Engineering Science and Technology Congress (ESTCON)
PublisherAIP Publishing
ISBN (Electronic)9780735417618
DOIs
Publication statusPublished - 13 Nov 2018
Event6th International Conference on Production, Energy and Reliability 2018 - Kuala Lumpur, Malaysia
Duration: 13 Aug 201814 Aug 2018

Publication series

NameAIP Conference Proceedings
PublisherAIP Publishing
Number1
Volume2035
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference6th International Conference on Production, Energy and Reliability 2018
Abbreviated titleICPER 2018
CountryMalaysia
CityKuala Lumpur
Period13/08/1814/08/18

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Keywords

  • Measure Correlate Predict with Principal Component Analysis (MCP i PCA)
  • Prediction

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Zakaria, AA., Fruh, W-G., & Ismail, F. B. (2018). Wind resource forecasting using enhanced measure correlate predict (MCP). In 6th International Conference on Production, Energy and Reliability 2018: World Engineering Science and Technology Congress (ESTCON) [040005] (AIP Conference Proceedings; Vol. 2035, No. 1). AIP Publishing. https://doi.org/10.1063/1.5075569