HVAC Power Conservation through Reverse Auctions and Machine Learning
Abstract
Prolonged rotating outages and exorbitant energy bills, recently experienced in California and Texas, have exposed the limitations and need for modernizing electric power systems. The occurrence of such events is a consequence of peak loads, often due to extreme outside temperatures that simultaneously trigger Heating Ventilation Air Conditioning (HVAC) systems. Leveraging pervasive computing technologies, such as smart meters and smart thermostats, this paper introduces a comprehensive approach to perform residential HVAC power conservation and prevent these catastrophic events. Differently from previous solutions, our approach models realistic user behavior and HVAC dynamics of individual homes. Specifically, we formulate a novel reverse auction-based problem, called POwer Conservation Optimization (POCO). The goal is to perform power conservation by motivating users to temporarily adjust their HVAC thermostat settings in exchange for financial rewards. We prove that POCO ensures truthfulness and individual rationality of the auction mechanism, although it is an NP-hard problem. Therefore, we propose an efficient heuristic, called Greedy Ranking AllocatioN (GRAN), which we prove ensures the same formal properties, while incurring only a polynomial complexity. To predict power savings resulting from an HVAC thermostat adjustments, we propose a novel machine learning-based technique called Power Saving Prediction (PSP). In addition, we conduct an online survey to study the willingness to adopt the proposed system and to model realistic user behavior. Survey results show willingness of adoption above 79% and a highly heterogeneous and non-linear user behavior. We perform extensive experiments using high-fidelity simulator EnergyPlus. Results show that PSP outperforms a state-of-The-Art solution obtaining 85% predictions within a 5% error margin. Furthermore, GRAN achieves near-optimal performance, outperforming a recent state-of-The-Art approach obtaining results between 58% and 68% closer to the optimum.
Recommended Citation
Casella, E., Khamesi, A. R., Silvestri, S., Baker, D. A., & Das, S. K. (2022). HVAC Power Conservation through Reverse Auctions and Machine Learning. 2022 IEEE International Conference on Pervasive Computing and Communications, PerCom 2022, pp. 89-100. Institute of Electrical and Electronics Engineers (IEEE).
The definitive version is available at https://doi.org/10.1109/PerCom53586.2022.9762402
Department(s)
Psychological Science
Second Department
Computer Science
Keywords and Phrases
Cyber-Physical Pervasive Computing; HVAC Power Conservation; Machine Learning Power Saving Predictions; Reverse Auctions; Smart Homes
International Standard Book Number (ISBN)
978-166541643-6
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2022 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2022
Comments
This work is partially supported by the National Institute for Food and Agriculture (NIFA) under the grant 2017-67008-26145; the NSF grants EPCN-1936131, CPS-1545037, and CNS-2008878; and the NSF CAREER grant CPS-1943035.