Citation

Nila, Farzana Sharmin and Tan, Wooi Haw and Ooi, Chee Pun and Umair, Muhammad and Tan, Yi Fei and Cheong, Soon Nyean (2025) Enhancing Energy Consumption Prediction by Integrating Occupant Activity with Machine Learning Models. International Journal of Integrated Engineering, 17 (2). ISSN 2229838X

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Abstract

The precision of the forecast of the power consumption of buildings is essential for big constructions in the present day. However, many of the models in use fail to consider the effect of people’s activities within the building on energy consumption. To overcome this limitation, this paper uses a synchronized data collection approach to collect data from different sensors about occupancy activity and power consumption. Several machine learning models are employed with this coordinated data, and the effects of occupant behaviour on power usage are explored. By analyzing the results of the models generated by the two algorithms, the best ways of reaching behaviour-sensitive power consumption prediction are determined. Therefore, the findings establish that the additional data concerning occupant activity provides more accurate assessments of energy usage that can be quite beneficial for enhancing the further development of better adaptive and more efficient building management systems. This work also helps to fill the existing gap in energy prediction literature wherein, unlike other fields, the human factor is considered in machine learning models that can lead to more accurate and less distortion-prone energy forecasting.

Item Type:	Article
Uncontrolled Keywords:	Synchronization, data collection, machine learning models, prediction,occupant activity1.IntroductionAccording to reports, about 30 to 40 percent of the world's energy consumption is within the building sector, while more than 80 percent of the used energy in a building happens in a building's entire lifespan, that is, the operational phase, which is inclusive of heating, cooling, ventilation, and lighting [1]. It, therefore, becomes very important to have predictions regarding a building's use of energy to make energy-efficient decisions. Use of data analytics: Lately , data analytics has made huge strides in development, such that new and advanced machine learning models are in place to be developed and deployed in the field of energy prediction [2]. As much as data analytics is improved, machine learning models have evolved to handle big data sets that capture several factors that could affect energy use—for example, environmental conditions, weather patterns, and equipment performances [3]. An artificial intelligence system employs machine learning, which is a guideline or procedure that enables the system to locate beneficial patterns and solutions within a predetermined amount of data, or it can be employed to predict output values depending on a certain amount of input values [4]. Machine learning, in turn, needs algorithms to learn [5]. One needs a set of data and then investigate the relationship between them, define patterns
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	06 Oct 2025 02:07
Last Modified:	06 Oct 2025 02:07
URII:	http://shdl.mmu.edu.my/id/eprint/14665

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