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Load forecasting for air conditioning systems using linear regression and artificial neural networks

The increasing demand for energy efficiency in industrial sectors necessitates innovative approaches to optimize energy consumption. This research addresses the challenge of accurately forecasting energy loads in air conditioning systems within the metal printing industry. Traditional forecasting m...

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Main Authors:	Zainudin, Zakaria Zikri, Yusoff, Siti Hajar, Gunawan, Teddy Surya, Mohamad, Sarah Yasmin, Chowdhury, Israth Jahan, Mohd Sapihie, Siti Nadiah
Format:	Proceeding Paper
Language:	English
Published:	IEEE 2024
Subjects:	TK1001 Production of electric energy. Powerplants TK2896 Production of electricity by direct energy conversion TK4001 Applications of electric power
Online Access:	http://irep.iium.edu.my/115178/7/115178_%20Load%20forecasting%20for%20air%20conditioning.pdf http://irep.iium.edu.my/115178/ https://ieeexplore.ieee.org/document/10675547 https://doi.org/10.1109/ICSIMA62563.2024.10675547
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spelling	my.iium.irep.1151782024-10-22T03:26:32Z http://irep.iium.edu.my/115178/ Load forecasting for air conditioning systems using linear regression and artificial neural networks Zainudin, Zakaria Zikri Yusoff, Siti Hajar Gunawan, Teddy Surya Mohamad, Sarah Yasmin Chowdhury, Israth Jahan Mohd Sapihie, Siti Nadiah TK1001 Production of electric energy. Powerplants TK2896 Production of electricity by direct energy conversion TK4001 Applications of electric power The increasing demand for energy efficiency in industrial sectors necessitates innovative approaches to optimize energy consumption. This research addresses the challenge of accurately forecasting energy loads in air conditioning systems within the metal printing industry. Traditional forecasting methods often fail to capture industrial settings' complex, dynamic energy demands. This study aims to develop a precise load forecasting model by integrating Linear Regression (LR) and Artificial Neural Networks (ANN). Using real-world data from Kian Joo Can Factory Berhad, the ANN model demonstrated superior performance with a Mean Absolute Percentage Error (MAPE) of 11.44% and a Coefficient of Variation of the Root Mean Square Error (CVRMSE) of 4.214%. These findings suggest significant potential for reducing energy consumption, lowering operational costs, and improving equipment maintenance. Implementing machine learning algorithms in this context underscores their value in enhancing the efficiency, reliability, and cost-effectiveness of Air Handling Units (AHU) in industrial air conditioning systems. IEEE 2024-09-18 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/115178/7/115178_%20Load%20forecasting%20for%20air%20conditioning.pdf Zainudin, Zakaria Zikri and Yusoff, Siti Hajar and Gunawan, Teddy Surya and Mohamad, Sarah Yasmin and Chowdhury, Israth Jahan and Mohd Sapihie, Siti Nadiah (2024) Load forecasting for air conditioning systems using linear regression and artificial neural networks. In: IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications, 30-31 July 2024, Bandung, Indonesia. https://ieeexplore.ieee.org/document/10675547 https://doi.org/10.1109/ICSIMA62563.2024.10675547
institution	Universiti Islam Antarabangsa Malaysia
building	IIUM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	International Islamic University Malaysia
content_source	IIUM Repository (IREP)
url_provider	http://irep.iium.edu.my/
language	English
topic	TK1001 Production of electric energy. Powerplants TK2896 Production of electricity by direct energy conversion TK4001 Applications of electric power
spellingShingle	TK1001 Production of electric energy. Powerplants TK2896 Production of electricity by direct energy conversion TK4001 Applications of electric power Zainudin, Zakaria Zikri Yusoff, Siti Hajar Gunawan, Teddy Surya Mohamad, Sarah Yasmin Chowdhury, Israth Jahan Mohd Sapihie, Siti Nadiah Load forecasting for air conditioning systems using linear regression and artificial neural networks
description	The increasing demand for energy efficiency in industrial sectors necessitates innovative approaches to optimize energy consumption. This research addresses the challenge of accurately forecasting energy loads in air conditioning systems within the metal printing industry. Traditional forecasting methods often fail to capture industrial settings' complex, dynamic energy demands. This study aims to develop a precise load forecasting model by integrating Linear Regression (LR) and Artificial Neural Networks (ANN). Using real-world data from Kian Joo Can Factory Berhad, the ANN model demonstrated superior performance with a Mean Absolute Percentage Error (MAPE) of 11.44% and a Coefficient of Variation of the Root Mean Square Error (CVRMSE) of 4.214%. These findings suggest significant potential for reducing energy consumption, lowering operational costs, and improving equipment maintenance. Implementing machine learning algorithms in this context underscores their value in enhancing the efficiency, reliability, and cost-effectiveness of Air Handling Units (AHU) in industrial air conditioning systems.
format	Proceeding Paper
author	Zainudin, Zakaria Zikri Yusoff, Siti Hajar Gunawan, Teddy Surya Mohamad, Sarah Yasmin Chowdhury, Israth Jahan Mohd Sapihie, Siti Nadiah
author_facet	Zainudin, Zakaria Zikri Yusoff, Siti Hajar Gunawan, Teddy Surya Mohamad, Sarah Yasmin Chowdhury, Israth Jahan Mohd Sapihie, Siti Nadiah
author_sort	Zainudin, Zakaria Zikri
title	Load forecasting for air conditioning systems using linear regression and artificial neural networks
title_short	Load forecasting for air conditioning systems using linear regression and artificial neural networks
title_full	Load forecasting for air conditioning systems using linear regression and artificial neural networks
title_fullStr	Load forecasting for air conditioning systems using linear regression and artificial neural networks
title_full_unstemmed	Load forecasting for air conditioning systems using linear regression and artificial neural networks
title_sort	load forecasting for air conditioning systems using linear regression and artificial neural networks
publisher	IEEE
publishDate	2024
url	http://irep.iium.edu.my/115178/7/115178_%20Load%20forecasting%20for%20air%20conditioning.pdf http://irep.iium.edu.my/115178/ https://ieeexplore.ieee.org/document/10675547 https://doi.org/10.1109/ICSIMA62563.2024.10675547
_version_	1814042756784324608
score	13.211869

Load forecasting for air conditioning systems using linear regression and artificial neural networks

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