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HVAC Scheduling based on Wi-Fi derived Occupancy

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BuildSys '20: Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

Pages 340 - 341

https://doi.org/10.1145/3408308.3431134

Published: 18 November 2020 Publication History

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Abstract

This poster showcases the integration of building management system data with Wi-Fi derived occupancy data to calibrate a building energy model for energy performance prediction and analysis. Our results show an overall improved building energy savings and unmet hours by the Wi-Fi derived schedules compared to the baseline and at 4% occupied threshold, similar energy consumption as baseline is achieved but with reduced unmet hours.

References

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2007. Energy Standard for Buildings Except Low-Rise Residential Buildings. American Society of Heating, Refrigerating and Air-Conditioning Engineers.

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U.S. Energy Information Administration 2016. Commercial Buildings Energy Consumption Survey (CBECS) 2016. U.S. Energy Information Administration. Retrieved October 11, 2020 from https://www.eia.gov/consumption/commercial/data/2012/pdf/user_guide_public_use_aug2016.pdf

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T. Labeodan, W. Zeiler, G. Boxem, and Y. Zhao. 2015. Occupancy measurement in commercial office buildings for demand-driven control applications---A survey and detection system evaluation. Energy and Buildings 93 (2015), 303--314.

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A. Trivedi, J. Gummeson, D. Irwin, D. Ganesan, and P. Shenoy. 2017. ISchedule: Campus-Scale HVAC Scheduling via Mobile WiFi Monitoring. In Proc. of the 8th Int'l Conf on Future Energy Systems (Shatin, Hong Kong) (e-Energy '17). ACM, New York, NY, USA, 132--142.

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Cited By

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Nikseresht FCampbell B(2023)FTM-Sense: Robust Sensor-free Occupancy Sensing Leveraging WiFi Fine Time MeasurementProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623741(140-148)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623741
Prabowo AChen KXue HSethuvenkatraman SSalim F(2023)Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learningProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623726(41-50)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623726
Chhaglani BZakaria CGummeson JShenoy PHossein Motlagh NZaidan MRebeiro-Hargrave AArvind D(2023)BreathEasy: Exploring the Potential of Acoustic Sensing for Healthy Indoor EnvironmentsProceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities10.1145/3597064.3597338(25-30)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3597064.3597338
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HVAC Scheduling based on Wi-Fi derived Occupancy

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Published In

BuildSys '20: Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 2020

361 pages

ISBN:9781450380614

DOI:10.1145/3408308

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 18 November 2020

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BuildSys '20

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SIGEnergy

BuildSys '20: The 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 18 - 20, 2020

Virtual Event, Japan

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BuildSys '20 Paper Acceptance Rate 38 of 139 submissions, 27%;

Overall Acceptance Rate 148 of 500 submissions, 30%

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Nikseresht FCampbell B(2023)FTM-Sense: Robust Sensor-free Occupancy Sensing Leveraging WiFi Fine Time MeasurementProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623741(140-148)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623741
Prabowo AChen KXue HSethuvenkatraman SSalim F(2023)Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learningProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623726(41-50)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623726
Chhaglani BZakaria CGummeson JShenoy PHossein Motlagh NZaidan MRebeiro-Hargrave AArvind D(2023)BreathEasy: Exploring the Potential of Acoustic Sensing for Healthy Indoor EnvironmentsProceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities10.1145/3597064.3597338(25-30)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3597064.3597338
Miller CPicchetti BFu CPantelic J(2022)Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysisScience and Technology for the Built Environment10.1080/23744731.2022.206746628:5(610-627)Online publication date: 3-May-2022
https://doi.org/10.1080/23744731.2022.2067466

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A Mechanism on Energy Harvesting and Data Communications in Wi-Fi Network

Saving Energy in Homes Using Wi-Fi Device Usage Patterns

Energy-efficient scheduling in IoT using Wi-Fi and ZigBee cross-technology

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