Application of Extreme Learning Machine in Predicting Short- Term Wind Speed
Electric load dispatching; Electric power transmission networks; Forecasting; Knowledge acquisition; Machine learning; Mean square error; Speed; Wind power; Developed model; Economic advantages; Extreme learning machine; Prediction accuracy; Root mean square errors; Scientific basis; Strong stabilit...
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Institute of Electrical and Electronics Engineers Inc.
2023
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my.uniten.dspace-253042023-05-29T16:08:00Z Application of Extreme Learning Machine in Predicting Short- Term Wind Speed Chen C.P. Tiong S.K. Koh S.P. Nasser A.A. Abbas D. Chooi F.Y. 25824552100 15128307800 22951210700 57220806149 57218304981 57220806223 Electric load dispatching; Electric power transmission networks; Forecasting; Knowledge acquisition; Machine learning; Mean square error; Speed; Wind power; Developed model; Economic advantages; Extreme learning machine; Prediction accuracy; Root mean square errors; Scientific basis; Strong stability; Sustainable resources; Wind At present, wind energy is the fastest growing power generation sector with its economic advantage of being a rich, clean and environmentally sustainable resources. However, wind does not generally blow consistently which prevents wind turbines from functioning at maximum capacity and capability. However, the solution that had been put forward in this paper to overcome the aforementioned problem is able to be used to make the deterministic predictions study for the wind speed, and of which its model in this paper possesses a significant prediction accuracy and strong stability, which could be useful in predicting the randomness of short term wind speed accurately. Based on the prediction output results, the amount of power required to be generated for load dispatch planning could be calculated and used to produce a scientific basis with the purpose of designing an optimal power grid dispatching design. In this paper, Extreme Learning Machine (ELM) is used for predicting short-term wind speed, and through use of the ELM the prediction accuracy of wind speed was observed to be at 0.93 followed by the root mean square error rate at 1.9. With reference to the prediction results, the developed model is tested to be able to predict wind speed accurately. � 2020 IEEE. Final 2023-05-29T08:08:00Z 2023-05-29T08:08:00Z 2020 Conference Paper 10.1109/ICIMU49871.2020.9243315 2-s2.0-85097652617 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097652617&doi=10.1109%2fICIMU49871.2020.9243315&partnerID=40&md5=6e5f1e2e30f74b8a7611ccea1e112ae6 https://irepository.uniten.edu.my/handle/123456789/25304 9243315 194 199 Institute of Electrical and Electronics Engineers Inc. Scopus |
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| description |
Electric load dispatching; Electric power transmission networks; Forecasting; Knowledge acquisition; Machine learning; Mean square error; Speed; Wind power; Developed model; Economic advantages; Extreme learning machine; Prediction accuracy; Root mean square errors; Scientific basis; Strong stability; Sustainable resources; Wind |
| author2 |
25824552100 |
| author_facet |
25824552100 Chen C.P. Tiong S.K. Koh S.P. Nasser A.A. Abbas D. Chooi F.Y. |
| format |
Conference Paper |
| author |
Chen C.P. Tiong S.K. Koh S.P. Nasser A.A. Abbas D. Chooi F.Y. |
| spellingShingle |
Chen C.P. Tiong S.K. Koh S.P. Nasser A.A. Abbas D. Chooi F.Y. Application of Extreme Learning Machine in Predicting Short- Term Wind Speed |
| author_sort |
Chen C.P. |
| title |
Application of Extreme Learning Machine in Predicting Short- Term Wind Speed |
| title_short |
Application of Extreme Learning Machine in Predicting Short- Term Wind Speed |
| title_full |
Application of Extreme Learning Machine in Predicting Short- Term Wind Speed |
| title_fullStr |
Application of Extreme Learning Machine in Predicting Short- Term Wind Speed |
| title_full_unstemmed |
Application of Extreme Learning Machine in Predicting Short- Term Wind Speed |
| title_sort |
application of extreme learning machine in predicting short- term wind speed |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806426300712222720 |
| score |
13.188404 |