research-article

Forecasting building occupancy using sensor network data

Authors:

William HoffAuthors Info & Claims

BigMine '13: Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications

Pages 87 - 94

https://doi.org/10.1145/2501221.2501233

Published: 11 August 2013 Publication History

Abstract

Forecasting the occupancy of buildings can lead to significant improvement of smart heating and cooling systems. Using a sensor network of simple passive infrared motion sensors densely placed throughout a building, we perform data mining to forecast occupancy a short time (i.e., up to 60 minutes) into the future. Our approach is to train a set of standard forecasting models to our time series data. Each model then forecasts occupancy a various horizons into the future. We combine these forecasts using a modified Bayesian combined forecasting approach. The method is demonstrated on two large building occupancy datasets, and shows promising results for forecasting horizons of up to 60 minutes. Because the two datasets have such different occupancy profiles, we compare our algorithms on each dataset to evaluate the performance of the forecasting algorithm for the different conditions.

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Cited By

Pan JCho TSun MDebnath RLonsdale NWilcox CBardhan R(2023)Future workspace needs flexibility and diversity: A machine learning-driven behavioural analysis of co-working spacePLOS ONE10.1371/journal.pone.029237018:10(e0292370)Online publication date: 18-Oct-2023
https://doi.org/10.1371/journal.pone.0292370
Chidurala V(2022)Occupancy Monitoring Using Low Resolution Thermal Imaging Sensorsundefined10.12794/metadc1987151Online publication date: Aug-2022
https://doi.org/10.12794/metadc1987151
Pan JCho TBardhan ROrtiz J(2022)Occupancy level prediction based on a sensor-detected dataset in a co-working spaceProceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3563357.3566133(340-347)Online publication date: 9-Nov-2022
https://dl.acm.org/doi/10.1145/3563357.3566133
Show More Cited By

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cover image ACM Conferences

BigMine '13: Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications

August 2013

119 pages

ISBN:9781450323246

DOI:10.1145/2501221

Program Chairs:
Wei Fan
Huawei Noah's Ark Lab
,
Albert Bifet
Yahoo! Research Barcelona
,
Qiang Yang
Huawei Noah's Ark Lab
,
Philip Yu
University of Illinois at Chicago

Copyright © 2013 ACM.

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: 11 August 2013

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KDD' 13

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KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 11, 2013

Illinois, Chicago

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BigMine '13 Paper Acceptance Rate 13 of 23 submissions, 57%;

Overall Acceptance Rate 13 of 23 submissions, 57%

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Cited By

Pan JCho TSun MDebnath RLonsdale NWilcox CBardhan R(2023)Future workspace needs flexibility and diversity: A machine learning-driven behavioural analysis of co-working spacePLOS ONE10.1371/journal.pone.029237018:10(e0292370)Online publication date: 18-Oct-2023
https://doi.org/10.1371/journal.pone.0292370
Chidurala V(2022)Occupancy Monitoring Using Low Resolution Thermal Imaging Sensorsundefined10.12794/metadc1987151Online publication date: Aug-2022
https://doi.org/10.12794/metadc1987151
Pan JCho TBardhan ROrtiz J(2022)Occupancy level prediction based on a sensor-detected dataset in a co-working spaceProceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3563357.3566133(340-347)Online publication date: 9-Nov-2022
https://dl.acm.org/doi/10.1145/3563357.3566133
Chidurala VLi X(2021)Occupancy Estimation Using Thermal Imaging Sensors and Machine Learning AlgorithmsIEEE Sensors Journal10.1109/JSEN.2021.304931121:6(8627-8638)Online publication date: 15-Mar-2021
https://doi.org/10.1109/JSEN.2021.3049311
Das AGupta RChakraborty S(2020)A Study on Real-Time Edge Computed Occupancy Estimation in an Indoor Environment2020 International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS48256.2020.9027463(527-530)Online publication date: Jan-2020
https://doi.org/10.1109/COMSNETS48256.2020.9027463
Ashouri ANewsham GShi ZGunay H(2019)Day-ahead Prediction of Building Occupancy using WiFi Signals2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)10.1109/COASE.2019.8843224(1237-1242)Online publication date: Aug-2019
https://doi.org/10.1109/COASE.2019.8843224
Duan KFong SZhuang YSong W(2018)Carbon Oxides Gases for Occupancy Counting and Emergency Control in Fog EnvironmentSymmetry10.3390/sym1003006610:3(66)Online publication date: 15-Mar-2018
https://doi.org/10.3390/sym10030066
Naylor SGillott MLau T(2018)A review of occupant-centric building control strategies to reduce building energy useRenewable and Sustainable Energy Reviews10.1016/j.rser.2018.07.01996(1-10)Online publication date: Nov-2018
https://doi.org/10.1016/j.rser.2018.07.019
Mulia MSupangkat SHariyanto N(2017)A review on building occupancy estimation methods2017 International Conference on ICT For Smart Society (ICISS)10.1109/ICTSS.2017.8288878(1-7)Online publication date: Sep-2017
https://doi.org/10.1109/ICTSS.2017.8288878
Aragon VGauthier SWarren PJames PAnderson B(2017)Developing English domestic occupancy profilesBuilding Research & Information10.1080/09613218.2017.139971947:4(375-393)Online publication date: 28-Nov-2017
https://doi.org/10.1080/09613218.2017.1399719
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