Enhancing Energy Consumption Prediction by Integrating Occupant Activity with Machine Learning Models - Farzana Sharmin Nila; Wooi-Haw Tan; Chee-Pun Ooi; Muhammad Umair; Yi-Fei Tan; Soon-Nyean Cheong (International Journal of Integrated Engineering, 2025)
The precision of the forecast of the power consumption of buildings isessential for big constructions in the present day. However, many ofthe models in use fail to consider the effect of people’s activitieswithin the building on energy consumption. To overcome thislimitation, this paper uses a synchronized data collection approach tocollect data from different sensors about occupancy activity andpower consumption. Several machine learning models are employedwith this coordinated data, and the effects of occupant behaviour onpower usage are explored. By analyzing the results of the modelsgenerated by the two algorithms, the best ways of reachingbehaviour-sensitive power consumption prediction are determined.Therefore, the findings establish that the additional data concerningoccupant activity provides more accurate assessments of energyusage that can be quite beneficial for enhancing the furtherdevelopment of better adaptive and more efficient buildingmanagement systems. This work also helps to fill the existing gap inenergy prediction literature wherein, unlike other fields, the humanfactor is considered in machine learning models that can lead to moreaccurate and more immune to distortion energy forecasting.