Predicting energy saving in air conditioning and mechanical ventilation systems by optimising the air-side parameters for different time zones

Zulkafli, Nur Izyan and Sukri, Mohamad Firdaus and Mohd Tahir, Musthafah and Sulaima, Mohamad Fani and Hanak, Dawid Piotr (2025) Predicting energy saving in air conditioning and mechanical ventilation systems by optimising the air-side parameters for different time zones. Journal of Sustainable Development of Energy, Water and Environment Systems, 13 (4). pp. 1-20. ISSN 1848-9257

Text
0219002122025105172607.pdf
Available under License Creative Commons Attribution.
Download (1MB)

Official URL: https://www.sdewes.org/jsdewes/pid13.0615

Abstract

This study proposed a prediction model for power consumption using linear programming to optimise return air and supply air temperatures while minimising total power consumption across different time zones. A multiple linear regression training analysis identified the correlation between power consumption and five air-side parameters, namely are the ambient temperature, return air temperature, supply air temperature, and humidity ratios. The results indicated a strong correlation and low root mean squared error, suggesting that these parameters significantly influence power consumption and provide a better-fitting model. From the optimal results of the linear programming model, optimised supply air temperature ranged from 17 °C to 18 °C, with return air temperature consistently at 21 °C, achieving a 4.26% energy saving compared to actual power consumption. In conclusion, the optimised values for return and supply air temperatures can be used to manage air temperature resets for the efficient operation of the air conditioning and mechanical ventilation system.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, Multiple linear regression, Linear programming model, Air conditioning, Ventilation system, Ambient temperature.
Divisions:	Faculty Of Mechanical Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	13 May 2026 01:59
Last Modified:	13 May 2026 01:59
URI:	http://eprints.utem.edu.my/id/eprint/29711
Statistic Details:	View Download Statistic

Actions (login required)

View Item