Killer whale-backpropagation (KW-BP) algorithm for accuracy improvement of neural network forecasting models on energy-efficient data

Green technology building is not newly introduced to the world nor Malaysia, but it is rarely practiced globally and now it has promoted noteworthy due to destructions caused by human hands towards the nature. Now people started to realize that the world is polluted by many hazardous substances. The...

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發表在:IAES International Journal of Artificial Intelligence
主要作者: 2-s2.0-85073501111
格式: Article
語言:English
出版: Institute of Advanced Engineering and Science 2019
在線閱讀:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073501111&doi=10.11591%2fijai.v8.i3.pp270-277&partnerID=40&md5=956907f8a467f17048720626352dfc86
id Kamaruddin S.B.A.; Ghani N.A.M.; Rahim H.A.; Musirin I.
spelling Kamaruddin S.B.A.; Ghani N.A.M.; Rahim H.A.; Musirin I.
2-s2.0-85073501111
Killer whale-backpropagation (KW-BP) algorithm for accuracy improvement of neural network forecasting models on energy-efficient data
2019
IAES International Journal of Artificial Intelligence
8
3
10.11591/ijai.v8.i3.pp270-277
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073501111&doi=10.11591%2fijai.v8.i3.pp270-277&partnerID=40&md5=956907f8a467f17048720626352dfc86
Green technology building is not newly introduced to the world nor Malaysia, but it is rarely practiced globally and now it has promoted noteworthy due to destructions caused by human hands towards the nature. Now people started to realize that the world is polluted by many hazardous substances. Therefore, Help University came up with the effort of preserving the nature through a new Green Technology campus, which has been fully operated since year 2017. In this research, neural network forecasting models on energy-efficient data of Help University, Subang 2 green technology campus at Subang Bistari, Selangor has been done with respect to value-for-money (VFM) attribute. Previously there were no similar research done on energy-efficient data of Help University, Subang 2 campus. The significant factors with respect to energy or electricity saved (MW/hr) in the year 2017 variable were studied as recorded by Building Automation and Control System (BAS) of Help University Subang 2 campus. Using multiple linear regression (stepwise method), the significant predictor towards energy saved (MW/hr) was Building Energy Index (BEI) (kWh/m2/year) based p-value<α=0.05. A mathematical model was developed. Moreover, the proposed neural network forecasting model using Killer Whale-Backpropagation Algorithm (KWBP) were found to better than existing conventional techniques to forecast BEI data. This research is expected to specifically assist maintenance department of Help University, Subang 2 campus towards load forecasting for power saving planning in years to come. © 2019 Institute of Advanced Engineering and Science. All rights reserved.
Institute of Advanced Engineering and Science
20894872
English
Article
All Open Access; Gold Open Access; Green Open Access
author 2-s2.0-85073501111
spellingShingle 2-s2.0-85073501111
Killer whale-backpropagation (KW-BP) algorithm for accuracy improvement of neural network forecasting models on energy-efficient data
author_facet 2-s2.0-85073501111
author_sort 2-s2.0-85073501111
title Killer whale-backpropagation (KW-BP) algorithm for accuracy improvement of neural network forecasting models on energy-efficient data
title_short Killer whale-backpropagation (KW-BP) algorithm for accuracy improvement of neural network forecasting models on energy-efficient data
title_full Killer whale-backpropagation (KW-BP) algorithm for accuracy improvement of neural network forecasting models on energy-efficient data
title_fullStr Killer whale-backpropagation (KW-BP) algorithm for accuracy improvement of neural network forecasting models on energy-efficient data
title_full_unstemmed Killer whale-backpropagation (KW-BP) algorithm for accuracy improvement of neural network forecasting models on energy-efficient data
title_sort Killer whale-backpropagation (KW-BP) algorithm for accuracy improvement of neural network forecasting models on energy-efficient data
publishDate 2019
container_title IAES International Journal of Artificial Intelligence
container_volume 8
container_issue 3
doi_str_mv 10.11591/ijai.v8.i3.pp270-277
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073501111&doi=10.11591%2fijai.v8.i3.pp270-277&partnerID=40&md5=956907f8a467f17048720626352dfc86
description Green technology building is not newly introduced to the world nor Malaysia, but it is rarely practiced globally and now it has promoted noteworthy due to destructions caused by human hands towards the nature. Now people started to realize that the world is polluted by many hazardous substances. Therefore, Help University came up with the effort of preserving the nature through a new Green Technology campus, which has been fully operated since year 2017. In this research, neural network forecasting models on energy-efficient data of Help University, Subang 2 green technology campus at Subang Bistari, Selangor has been done with respect to value-for-money (VFM) attribute. Previously there were no similar research done on energy-efficient data of Help University, Subang 2 campus. The significant factors with respect to energy or electricity saved (MW/hr) in the year 2017 variable were studied as recorded by Building Automation and Control System (BAS) of Help University Subang 2 campus. Using multiple linear regression (stepwise method), the significant predictor towards energy saved (MW/hr) was Building Energy Index (BEI) (kWh/m2/year) based p-value<α=0.05. A mathematical model was developed. Moreover, the proposed neural network forecasting model using Killer Whale-Backpropagation Algorithm (KWBP) were found to better than existing conventional techniques to forecast BEI data. This research is expected to specifically assist maintenance department of Help University, Subang 2 campus towards load forecasting for power saving planning in years to come. © 2019 Institute of Advanced Engineering and Science. All rights reserved.
publisher Institute of Advanced Engineering and Science
issn 20894872
language English
format Article
accesstype All Open Access; Gold Open Access; Green Open Access
record_format scopus
collection Scopus
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