Particle Swarm Optimization for NARX-ANN Baseline Energy Modelling in Educational Buildings

Multiple Linear Regression (MLR) model and Non-Linear Auto-Regressive with Exogenous Input encompass Artificial Neural Network (NARX-ANN) have been widely used for energy consumption prediction. The main purpose of the prediction is to estimate potential energy saving in the building from various en...

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Published in:2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings
Main Author: Mustapa R.F.; Dahlan N.Y.; Yassin I.M.; Hamizah Mohd Nordin A.; Mahadan M.E.; Mohamad S.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2019
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072911285&doi=10.1109%2fI2CACIS.2019.8825097&partnerID=40&md5=39e535195d914097c804da092f46ce59
id 2-s2.0-85072911285
spelling 2-s2.0-85072911285
Mustapa R.F.; Dahlan N.Y.; Yassin I.M.; Hamizah Mohd Nordin A.; Mahadan M.E.; Mohamad S.
Particle Swarm Optimization for NARX-ANN Baseline Energy Modelling in Educational Buildings
2019
2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings


10.1109/I2CACIS.2019.8825097
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072911285&doi=10.1109%2fI2CACIS.2019.8825097&partnerID=40&md5=39e535195d914097c804da092f46ce59
Multiple Linear Regression (MLR) model and Non-Linear Auto-Regressive with Exogenous Input encompass Artificial Neural Network (NARX-ANN) have been widely used for energy consumption prediction. The main purpose of the prediction is to estimate potential energy saving in the building from various energy management and retrofit programs. Accurate prediction from a precise baseline energy model are important because they ensure energy savings is correctly determined. MLR and NARX-ANN have gained popularity in modeling and prediction due to thier user-friendly and simple approach. Even though NARX-ANN offers simplicity, certain limitations exist within its architecture and often associated with optimization problems. Furthermore, the MLR prediction is suitable for a system that behaves linearly. Thus, the main intention of this paper is to hybrid the NARX-ANN technique with Particle Swarm Optimization (PSO) technique for energy consumption prediction and baseline energy modeling. NARX-ANN architecture is optimized with PSO technique and the predicted outcome is compared with the actual value. The modeling error is compared between MLR and the non-optimized NARX-ANN model. The baseline energy consumption is developed for two buildings in a university compound. It is found that NARX-ANN-PSO optimization performs better in terms of error measurement. From this work, the proposed baseline energy models can be used to predict energy consumption hence determine savings accurately. © 2019 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Mustapa R.F.; Dahlan N.Y.; Yassin I.M.; Hamizah Mohd Nordin A.; Mahadan M.E.; Mohamad S.
spellingShingle Mustapa R.F.; Dahlan N.Y.; Yassin I.M.; Hamizah Mohd Nordin A.; Mahadan M.E.; Mohamad S.
Particle Swarm Optimization for NARX-ANN Baseline Energy Modelling in Educational Buildings
author_facet Mustapa R.F.; Dahlan N.Y.; Yassin I.M.; Hamizah Mohd Nordin A.; Mahadan M.E.; Mohamad S.
author_sort Mustapa R.F.; Dahlan N.Y.; Yassin I.M.; Hamizah Mohd Nordin A.; Mahadan M.E.; Mohamad S.
title Particle Swarm Optimization for NARX-ANN Baseline Energy Modelling in Educational Buildings
title_short Particle Swarm Optimization for NARX-ANN Baseline Energy Modelling in Educational Buildings
title_full Particle Swarm Optimization for NARX-ANN Baseline Energy Modelling in Educational Buildings
title_fullStr Particle Swarm Optimization for NARX-ANN Baseline Energy Modelling in Educational Buildings
title_full_unstemmed Particle Swarm Optimization for NARX-ANN Baseline Energy Modelling in Educational Buildings
title_sort Particle Swarm Optimization for NARX-ANN Baseline Energy Modelling in Educational Buildings
publishDate 2019
container_title 2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings
container_volume
container_issue
doi_str_mv 10.1109/I2CACIS.2019.8825097
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072911285&doi=10.1109%2fI2CACIS.2019.8825097&partnerID=40&md5=39e535195d914097c804da092f46ce59
description Multiple Linear Regression (MLR) model and Non-Linear Auto-Regressive with Exogenous Input encompass Artificial Neural Network (NARX-ANN) have been widely used for energy consumption prediction. The main purpose of the prediction is to estimate potential energy saving in the building from various energy management and retrofit programs. Accurate prediction from a precise baseline energy model are important because they ensure energy savings is correctly determined. MLR and NARX-ANN have gained popularity in modeling and prediction due to thier user-friendly and simple approach. Even though NARX-ANN offers simplicity, certain limitations exist within its architecture and often associated with optimization problems. Furthermore, the MLR prediction is suitable for a system that behaves linearly. Thus, the main intention of this paper is to hybrid the NARX-ANN technique with Particle Swarm Optimization (PSO) technique for energy consumption prediction and baseline energy modeling. NARX-ANN architecture is optimized with PSO technique and the predicted outcome is compared with the actual value. The modeling error is compared between MLR and the non-optimized NARX-ANN model. The baseline energy consumption is developed for two buildings in a university compound. It is found that NARX-ANN-PSO optimization performs better in terms of error measurement. From this work, the proposed baseline energy models can be used to predict energy consumption hence determine savings accurately. © 2019 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
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record_format scopus
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