Improving Building Temperature Forecasting: A Data-driven Approach with System Scenario Clustering

arXiv:2402.13628v1 Announce Type: new
Abstract: Heat, Ventilation and Air Conditioning (HVAC) systems play a critical role in maintaining a comfortable thermal environment and cost approximately 40% of primary energy usage in the building sector. For smart energy management in buildings, usage patterns and their resulting profiles allow the improvement of control systems with prediction capabilities. However, for large-scale HVAC system management, it is difficult to construct a detailed model for each subsystem. In this paper, a new data-driven room temperature …

abstract air conditioning arxiv building buildings clustering cost cs.lg data data-driven eess.sp energy energy management environment forecasting heat hvac improvement management patterns profiles role sector smart systems type usage