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 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 more immune to distortion energy forecasting.