Short Term Wind Speed Forecasting : A Machine Learning Based Predictive Analytics

Annael J. Domingo, Felan Carlo Garcia, Mary Lai Salvana, Dr. Nathaniel J. C. Libatique, Dr. Gregory L. Tangonan

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

4 Scopus citations


The challenges posed by the intermittence and uncertainty of renewable energy due to its variability and limited storage require accurate forecasts for economies looking to source a significant amount of energy from renewables. We report on the use of several supervised learning models such as Random Forest, Extremely Randomized Trees, Support Vector Regression and k-Nearest Neighbors Regression to forecast ahead of time wind speed measurements using data from the wind met masts located at Buguey, Ballesteros and Sta. Ana, Cagayan. Results show that in terms of predicting the next hour wind speed measurements for one day, the k-NNR model outperforms the other three models while the ET model have shown the highest predictive performance among the four models in prediction of the next hour wind speed measurements for one month and 20% of the total data. It is anticipated that the proposed ET model can be used as an effective wind speed prediction model as well as the k-NNR model. The common perception by energy companies in ASEAN that RE output is unpredictable needs to be rethought in the sight of the new AI techniques.
Original languageEnglish (US)
Title of host publicationTENCON 2018 - 2018 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Print)9781538654576
StatePublished - Oct 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We are grateful to the Engineering for Research, Development and Technology (ERDT) Program of the Department of Science and Technology for student fellowship support. One of the authors would also like to acknowledge an Ateneo chair from the Roque Ma. Gonzales Science Endowment fund. We would also like to acknowledge the previous team who started this using other methods, Carlo
Mallari, Sherdon Niño Uy and Angeli Silang as well as Felan Carlo Garcia who helped us learn the machine learning
concept for regression.


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