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Optimal HVAC building control with occupancy prediction

Published: 03 November 2014 Publication History

Abstract

Buildings account for about 41% of primary energy consumption and 75% of the electricity. Space heating, space cooling, and ventilation are the dominant end uses, accounting for 41% of all energy consumed in the buildings sector. Growing interest in sustainability has resulted in research efforts to reduce energy consumption while providing adequate comfort to users.
In this work, we present a Model Predictive Control (MPC) framework for optimal HVAC control that minimizes energy consumption while staying within the comfort bounds of the occupants. The novelty of our approach lies in the use of prediction occupancy models derived from data traces and incorporating those models within the MPC framework. We use a Blended Markov Chain (BMC) occupancy prediction model in order to predict thermal load and occupancy of each zone in the building. We test our approach in simulation and compare it with occupancy schedules and control rules currently use in our university buildings. Our preliminary results show that 15.5% savings in cooling in the summer, and 9.4% savings in heating in the winter are achievable when conditioning the building using our MPC/BMC control framework.

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ASHRAE standard 55: Thermal environmental conditions for human occupancy. ASHRAE,Inc., 2004.
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V. Erickson and A. Cerpa. Occupancy based demand response HVAC control strategy. In BuildSys, 2010.
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V. L. Erickson, M. Á. Carreira-Perpiñán, and A. E. Cerpa. OBSERVE: Occupancy-based system for efficient reduction of HVAC energy. In IPSN'11.
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G. P. Henze, C. Felsmann, and G. Knabe. Evaluation of optimal control for active and passive building thermal storage. International Journal of Thermal Sciences, 43(2):173--183, 2004.
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A. Kelman, Y. Ma, and F. Borrelli. Analysis of local optima in predictive control for energy efficient buildings. Journal of Building Performance Simulation, 6(3):236--255, 2013.
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J. Lu, T. Sookoor, V. Srinivasan, G. Gao, B. Holben, J. Stankovic, E. Field, and K. Whitehouse. The smart thermostat: using occupancy sensors to save energy in homes. In SenSys, 2010.
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D. Q. Mayne, J. B. Rawlings, C. V. Rao, and P. O. Scokaert. Constrained model predictive control: Stability and optimality. Automatica, 36(6):789--814, 2000.
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Cited By

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  • (2024)Mapping Thermal Footprints: Occupancy Estimation and Localization in Diverse Indoor Settings with Thermal ArraysProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675059(38-49)Online publication date: 8-Jul-2024
  • (2024)Exploring Deep Reinforcement Learning for Holistic Smart Building ControlACM Transactions on Sensor Networks10.1145/365604320:3(1-28)Online publication date: 2-Apr-2024
  • (2024)PhyGICS – A Physics-informed Graph Neural Network-based Intelligent HVAC Controller for Open-plan SpacesProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661957(203-214)Online publication date: 4-Jun-2024
  • Show More Cited By

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cover image ACM Conferences
BuildSys '14: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings
November 2014
241 pages
ISBN:9781450331449
DOI:10.1145/2674061
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 03 November 2014

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Author Tags

  1. HVAC
  2. model predictive control
  3. occupancy

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Overall Acceptance Rate 148 of 500 submissions, 30%

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Cited By

View all
  • (2024)Mapping Thermal Footprints: Occupancy Estimation and Localization in Diverse Indoor Settings with Thermal ArraysProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675059(38-49)Online publication date: 8-Jul-2024
  • (2024)Exploring Deep Reinforcement Learning for Holistic Smart Building ControlACM Transactions on Sensor Networks10.1145/365604320:3(1-28)Online publication date: 2-Apr-2024
  • (2024)PhyGICS – A Physics-informed Graph Neural Network-based Intelligent HVAC Controller for Open-plan SpacesProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661957(203-214)Online publication date: 4-Jun-2024
  • (2024)Multi-agent Reinforcement Learning for Joint Control of EV-HVAC System with Vehicle-to-Building SupplyProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632421(332-341)Online publication date: 4-Jan-2024
  • (2024)Non-intrusive thermal load disaggregation and forecasting for effective HVAC systemsApplied Energy10.1016/j.apenergy.2024.123379367(123379)Online publication date: Aug-2024
  • (2023)A Fast Method for Calculating the Impact of Occupancy on Commercial Building Energy ConsumptionBuildings10.3390/buildings1302056713:2(567)Online publication date: 19-Feb-2023
  • (2022)Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive LearningSensors10.3390/s2209318622:9(3186)Online publication date: 21-Apr-2022
  • (2022)PACMANProceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3563357.3564052(11-20)Online publication date: 9-Nov-2022
  • (2022)FlowSenseProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35172586:1(1-26)Online publication date: 29-Mar-2022
  • (2022)Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems2022 IEEE Conference on Technologies for Sustainability (SusTech)10.1109/SusTech53338.2022.9794179(187-194)Online publication date: 21-Apr-2022
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