Data-Driven Occupancy Profile Identification and Application to the Ventilation Schedule in a School Building

Kristina Vassiljeva (), Margarita Matson, Andrea Ferrantelli, Eduard Petlenkov, Martin Thalfeldt and Juri Belikov
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Kristina Vassiljeva: FinEst Centre for Smart Cities (Finest Centre), Tallinn University of Technology, 19086 Tallinn, Estonia
Margarita Matson: FinEst Centre for Smart Cities (Finest Centre), Tallinn University of Technology, 19086 Tallinn, Estonia
Andrea Ferrantelli: FinEst Centre for Smart Cities (Finest Centre), Tallinn University of Technology, 19086 Tallinn, Estonia
Eduard Petlenkov: FinEst Centre for Smart Cities (Finest Centre), Tallinn University of Technology, 19086 Tallinn, Estonia
Martin Thalfeldt: FinEst Centre for Smart Cities (Finest Centre), Tallinn University of Technology, 19086 Tallinn, Estonia
Juri Belikov: FinEst Centre for Smart Cities (Finest Centre), Tallinn University of Technology, 19086 Tallinn, Estonia

Energies, 2024, vol. 17, issue 13, 1-23

Abstract: Facing the current sustainability challenges requires reduction in building stock energy usage towards achieving the European Green Deal targets. This can be accomplished by adopting techniques such as fault detection and diagnosis and efficiency optimization. Taking an Estonian school as a case study, an occupancy-based algorithm for scheduling ventilation operations in buildings is here developed starting only from energy use data. The aim is optimizing the system’s operation according to occupancy profiles while maintaining a comfortable indoor climate. By relying only on electricity meters without using carbon dioxide or occupancy sensors, we use the historical data of a school to develop a DBSCAN-based clustering algorithm that generates consumption profiles. A novel occupancy estimation algorithm, based on threshold and time-series methods, then creates 12 occupancy schedules that are either based on classical detection with an on-off method or on occupancy estimation for demand-controlled ventilation. We find that the latter replaces the 60% capacity of current on-off schedules by 30% or even 0%, with energy savings ranging from 3.5% to 66.4%. The corresponding costs are reduced from 18.1% up to 62.6%, while still complying with current national regulations for indoor air quality. Remarkably, our method can immediately be extended to other countries, as it relies only on occupancy schedules that ignore weather and other location-specific factors.

Keywords: AHU; HVAC; occupancy; data clustering; DBSCAN; energy efficiency; optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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