Enhancing the performance of building looad forecasting using hybrid of GLSSVM - ABC Model
In conducting load forecasting, the accuracy of forecasting is an important aspect in planning and managing electricity. Thus, a new hybrid model is presented in this paper, which combines the Group Method of Data Handling, Least Square Support Vector Machine and Artificial Bee Colony (GLSSVM- ABC)...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | Conference or Workshop Item |
Published: |
EDP Sciences
2016
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/73102/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986205366&doi=10.1051%2fmatecconf%2f20167010010&partnerID=40&md5=35ecf2fefdfc4d50e6f8e59a4eac189c |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.73102 |
---|---|
record_format |
eprints |
spelling |
my.utm.731022017-11-27T02:00:02Z http://eprints.utm.my/id/eprint/73102/ Enhancing the performance of building looad forecasting using hybrid of GLSSVM - ABC Model Daut, M. A. M. Ahmad, A. S. Hassan, M. Y. Abdullah, H. Abdullah, M. P. Husin, F. TK Electrical engineering. Electronics Nuclear engineering In conducting load forecasting, the accuracy of forecasting is an important aspect in planning and managing electricity. Thus, a new hybrid model is presented in this paper, which combines the Group Method of Data Handling, Least Square Support Vector Machine and Artificial Bee Colony (GLSSVM- ABC) for building load forecasting. Its performance accuracy has been compared with other methods by using the Mean Absolute Percentage Error (MAPE) and Root Means Square Error (RMSE). It was found that the proposed method has resulted in better performance accuracy in terms of both MAPE and RMSE. The MAPE analysis showed an increase in performance accuracy of more than 7 percent when compared to other methods. The RMSE analysis showed an increase in performance accuracy of more than 5 percent when compared to other methods. The results in this study showed that the proposed method is proven to be effective and has great potential for accurate building load forecasting. EDP Sciences 2016 Conference or Workshop Item PeerReviewed Daut, M. A. M. and Ahmad, A. S. and Hassan, M. Y. and Abdullah, H. and Abdullah, M. P. and Husin, F. (2016) Enhancing the performance of building looad forecasting using hybrid of GLSSVM - ABC Model. In: 2016 3rd International Conference on Manufacturing and Industrial Technologies, ICMIT 2016, 25 May 2016 through 27 May 2016, Turkey. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986205366&doi=10.1051%2fmatecconf%2f20167010010&partnerID=40&md5=35ecf2fefdfc4d50e6f8e59a4eac189c |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
topic |
TK Electrical engineering. Electronics Nuclear engineering |
spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering Daut, M. A. M. Ahmad, A. S. Hassan, M. Y. Abdullah, H. Abdullah, M. P. Husin, F. Enhancing the performance of building looad forecasting using hybrid of GLSSVM - ABC Model |
description |
In conducting load forecasting, the accuracy of forecasting is an important aspect in planning and managing electricity. Thus, a new hybrid model is presented in this paper, which combines the Group Method of Data Handling, Least Square Support Vector Machine and Artificial Bee Colony (GLSSVM- ABC) for building load forecasting. Its performance accuracy has been compared with other methods by using the Mean Absolute Percentage Error (MAPE) and Root Means Square Error (RMSE). It was found that the proposed method has resulted in better performance accuracy in terms of both MAPE and RMSE. The MAPE analysis showed an increase in performance accuracy of more than 7 percent when compared to other methods. The RMSE analysis showed an increase in performance accuracy of more than 5 percent when compared to other methods. The results in this study showed that the proposed method is proven to be effective and has great potential for accurate building load forecasting. |
format |
Conference or Workshop Item |
author |
Daut, M. A. M. Ahmad, A. S. Hassan, M. Y. Abdullah, H. Abdullah, M. P. Husin, F. |
author_facet |
Daut, M. A. M. Ahmad, A. S. Hassan, M. Y. Abdullah, H. Abdullah, M. P. Husin, F. |
author_sort |
Daut, M. A. M. |
title |
Enhancing the performance of building looad forecasting using hybrid of GLSSVM - ABC Model |
title_short |
Enhancing the performance of building looad forecasting using hybrid of GLSSVM - ABC Model |
title_full |
Enhancing the performance of building looad forecasting using hybrid of GLSSVM - ABC Model |
title_fullStr |
Enhancing the performance of building looad forecasting using hybrid of GLSSVM - ABC Model |
title_full_unstemmed |
Enhancing the performance of building looad forecasting using hybrid of GLSSVM - ABC Model |
title_sort |
enhancing the performance of building looad forecasting using hybrid of glssvm - abc model |
publisher |
EDP Sciences |
publishDate |
2016 |
url |
http://eprints.utm.my/id/eprint/73102/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986205366&doi=10.1051%2fmatecconf%2f20167010010&partnerID=40&md5=35ecf2fefdfc4d50e6f8e59a4eac189c |
_version_ |
1643656576135856128 |
score |
13.18916 |