Machine Learning Baseline Energy Model (MLBEM) to Evaluate Prediction Performances in Building Energy Consumption

Electric Energy Consumption (EEC) prediction for building operations can be performed using a Baseline Energy Model (BEM), which is vital to ensure the efficiency of the EEC estimates with its respective independent variables. However, developing the BEM to represent the relationship between indepen...

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Published in:Engineering, Technology and Applied Science Research
Main Author: Mustapa R.F.; Hairuddin M.A.; Nordin A.H.M.; Dahlan N.Y.; Yassin I.M.; Ashar N.D.K.
Format: Article
Language:English
Published: Dr D. Pylarinos 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203275548&doi=10.48084%2fetasr.7683&partnerID=40&md5=3804a40670b72432127e20a4e031c269
id 2-s2.0-85203275548
spelling 2-s2.0-85203275548
Mustapa R.F.; Hairuddin M.A.; Nordin A.H.M.; Dahlan N.Y.; Yassin I.M.; Ashar N.D.K.
Machine Learning Baseline Energy Model (MLBEM) to Evaluate Prediction Performances in Building Energy Consumption
2024
Engineering, Technology and Applied Science Research
14
4
10.48084/etasr.7683
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203275548&doi=10.48084%2fetasr.7683&partnerID=40&md5=3804a40670b72432127e20a4e031c269
Electric Energy Consumption (EEC) prediction for building operations can be performed using a Baseline Energy Model (BEM), which is vital to ensure the efficiency of the EEC estimates with its respective independent variables. However, developing the BEM to represent the relationship between independent variables can be a complex task due to the EEC variability in an educational building that differs during its operation period. The best-suited BEM must be continuously improvised to achieve good modeling with accurate and reliable predictions that capture the building operations’ current dynamics. This study aims to conduct a comparative performance assessment between deep learning, machine learning, and statistical models to develop the BEM and, therefore, predict the EEC of the building for 24, 48, 72, and 96 hours, while considering the operation of the lecture weeks and the associated number of students and staff. The hours and temperature are considered as independent variables to be tested with residual error evaluations, whilst the correlation coefficient, coefficient of determination, and training time are also takeninto account. Three models with different categories involving Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and AutoRegressive Integrated Moving Average with Exogenous inputs (ARIMAX) were compared, concluding that SVR was the best and can be used as a universal model in the Machine Learning Baseline Energy Model (MLBEM) studies. Accurate EEC prediction will offer a huge advantage for building operators to properly monitor, plan, and manage the EEC, hence avoiding excessive utility bills. © by the authors.
Dr D. Pylarinos
22414487
English
Article
All Open Access
author Mustapa R.F.; Hairuddin M.A.; Nordin A.H.M.; Dahlan N.Y.; Yassin I.M.; Ashar N.D.K.
spellingShingle Mustapa R.F.; Hairuddin M.A.; Nordin A.H.M.; Dahlan N.Y.; Yassin I.M.; Ashar N.D.K.
Machine Learning Baseline Energy Model (MLBEM) to Evaluate Prediction Performances in Building Energy Consumption
author_facet Mustapa R.F.; Hairuddin M.A.; Nordin A.H.M.; Dahlan N.Y.; Yassin I.M.; Ashar N.D.K.
author_sort Mustapa R.F.; Hairuddin M.A.; Nordin A.H.M.; Dahlan N.Y.; Yassin I.M.; Ashar N.D.K.
title Machine Learning Baseline Energy Model (MLBEM) to Evaluate Prediction Performances in Building Energy Consumption
title_short Machine Learning Baseline Energy Model (MLBEM) to Evaluate Prediction Performances in Building Energy Consumption
title_full Machine Learning Baseline Energy Model (MLBEM) to Evaluate Prediction Performances in Building Energy Consumption
title_fullStr Machine Learning Baseline Energy Model (MLBEM) to Evaluate Prediction Performances in Building Energy Consumption
title_full_unstemmed Machine Learning Baseline Energy Model (MLBEM) to Evaluate Prediction Performances in Building Energy Consumption
title_sort Machine Learning Baseline Energy Model (MLBEM) to Evaluate Prediction Performances in Building Energy Consumption
publishDate 2024
container_title Engineering, Technology and Applied Science Research
container_volume 14
container_issue 4
doi_str_mv 10.48084/etasr.7683
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203275548&doi=10.48084%2fetasr.7683&partnerID=40&md5=3804a40670b72432127e20a4e031c269
description Electric Energy Consumption (EEC) prediction for building operations can be performed using a Baseline Energy Model (BEM), which is vital to ensure the efficiency of the EEC estimates with its respective independent variables. However, developing the BEM to represent the relationship between independent variables can be a complex task due to the EEC variability in an educational building that differs during its operation period. The best-suited BEM must be continuously improvised to achieve good modeling with accurate and reliable predictions that capture the building operations’ current dynamics. This study aims to conduct a comparative performance assessment between deep learning, machine learning, and statistical models to develop the BEM and, therefore, predict the EEC of the building for 24, 48, 72, and 96 hours, while considering the operation of the lecture weeks and the associated number of students and staff. The hours and temperature are considered as independent variables to be tested with residual error evaluations, whilst the correlation coefficient, coefficient of determination, and training time are also takeninto account. Three models with different categories involving Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and AutoRegressive Integrated Moving Average with Exogenous inputs (ARIMAX) were compared, concluding that SVR was the best and can be used as a universal model in the Machine Learning Baseline Energy Model (MLBEM) studies. Accurate EEC prediction will offer a huge advantage for building operators to properly monitor, plan, and manage the EEC, hence avoiding excessive utility bills. © by the authors.
publisher Dr D. Pylarinos
issn 22414487
language English
format Article
accesstype All Open Access
record_format scopus
collection Scopus
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