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

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.

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Abstract

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.

Item Type:	Proceeding Paper (Slide Presentation)
Uncontrolled Keywords:	Load Forecasting, Machine Learning, Artificial Neural Network (ANN), HVAC Systems, Energy Management.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001 Production of electric energy. Powerplants T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2896 Production of electricity by direct energy conversion T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK4001 Applications of electric power
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Engineering
Depositing User:	dr siti hajar yusoff
Date Deposited:	22 Oct 2024 11:26
Last Modified:	22 Oct 2024 11:26
URI:	http://irep.iium.edu.my/id/eprint/115178

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