A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO2 Sensor Data
@article{Ang2018ASR, title={A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO2 Sensor Data}, author={Irvan Bastian Arief Ang and Margaret Hamilton and Flora D. Salim}, journal={ACM Transactions on Sensor Networks (TOSN)}, year={2018}, volume={14}, pages={1 - 28} }
Human occupancy counting is crucial for both space utilisation and building energy optimisation. [] Key Method DA-HOC++ is able to predict the number of occupants with minimal training data: as little as 1 day’s data. DA-HOC++ accurately predicts indoor human occupancy for five different rooms across different countries using a model trained from a small room and adapted to other rooms. We evaluate DA-HOC++ with two baseline methods: a support vector regression technique and an SD-HOC model. The results…
Figures and Tables from this paper
38 Citations
ODToolkit: A Toolkit for Building Occupancy Detection
- Computer Sciencee-Energy
- 2019
This paper extends the design and implementation of an open-source toolkit for occupancy detection by implementing novel domain-adaptive occupancy detection algorithms and comparing them with the benchmark supervised learning algorithms on multiple data sets.
Occupancy Prediction in Buildings: An approach leveraging LSTM and Federated Learning
- Engineering2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
- 2022
Nowadays, the energy used in commercial, residential, and office buildings represents a significant amount of the total energy spent worldwide. In these contexts, energy can be dramatically reduced…
Estimating Number and Dwell Time of Visitors from CO2 Concentration using Partial Modeling with Variable Time Window
- Engineering2020 Third International Conference on Artificial Intelligence for Industries (AI4I)
- 2020
This paper proposes partial modeling with a variable time window that can make a partial estimation model that automatically corresponds to differences in the change speed between two variables: visitors and CO2 concentration.
Estimation of Occupancy Using IoT Sensors and a Carbon Dioxide-Based Machine Learning Model with Ventilation System and Differential Pressure Data
- EngineeringSensors
- 2023
Infectious diseases such as the COVID-19 pandemic have necessitated preventive measures against the spread of indoor infections. There has been increasing interest in indoor air quality (IAQ)…
Occupancy Estimation Using Sparse Sensor Coverage
- Engineering, Computer ScienceIOT
- 2022
A method for estimating occupancy based on sparse coverage of low-cost IoT sensors, which shows that with less sensor coverage, sensor placement becomes more important and that even with 20% it is possible to get as good of an accuracy as full coverage.
Chameleon: Adaptive Sensor Intelligence for Smart Buildings
- Computer ScienceIEEE Internet of Things Journal
- 2022
Chameleon is presented, an adaptive sensor fusion and hybrid machine learning architecture that is able to classify room activity states with accuracies in the range of 87%–99% on test sets and is shown to be scalable due to minimal hardware and software requirements.
A State of Art Review on Methodologies of Occupancy Estimating in Buildings from 2011 to 2021
- EngineeringElectronics
- 2022
Occupancy information is important to building facility managers in terms of building energy efficiency, indoor environmental quality, comfort conditions, and safety management of buildings. When…
References
SHOWING 1-10 OF 45 REFERENCES
DA-HOC: semi-supervised domain adaptation for room occupancy prediction using CO2 sensor data
- PsychologyBuildSys@SenSys
- 2017
A semi-supervised domain adaptation method for carbon dioxide - Human Occupancy Counter (DA-HOC), a robust way to estimate the number of people within in one room by using data from a carbon dioxide sensor.
RUP: Large Room Utilisation Prediction with carbon dioxide sensor
- Computer SciencePervasive Mob. Comput.
- 2018
PerCCS: person-count from carbon dioxide using sparse non-negative matrix factorization
- Computer ScienceUbiComp
- 2015
The PerCCS algorithm is presented that explores the possibility of estimating person count from CO2 sensors already integrated in everyday room air-conditioning infrastructure by using task-driven Sparse Non-negative Matrix Factorization and outperformed the comparative methods in predicting occupancy count.
A multi-sensor based occupancy estimation model for supporting demand driven HVAC operations
- EngineeringANSS 2012
- 2012
The results indicate the ability of the proposed system to monitor the occupancy information of multi-occupancy spaces in real time, supporting demand driven HVAC operations.
Human occupancy recognition with multivariate ambient sensors
- Computer Science2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)
- 2016
This paper aims to identify which ambient sensor is the most dominant in recognising human presence, and explains the methodology for integrating large amounts of sensor data, and discusses the experiments and findings in relation to the binary occupancy of a single person office.
Real-time Building Occupancy Sensing for Supporting Demand Driven HVAC Operations
- Engineering
- 2013
Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for Heating, Ventilation and Air-conditioning (HVAC) systems. However, a precise and reliable…
SD-HOC: Seasonal Decomposition Algorithm for Mining Lagged Time Series
- Computer ScienceAusDM
- 2017
Seasonal Decomposition for Human Occupancy Counting (SD-HOC), a customised feature transformation decomposition, novel way to estimate the number of people within a closed space using only a single carbon dioxide sensor is presented.
Occupancy monitoring using environmental & context sensors and a hierarchical analysis framework
- Computer ScienceBuildSys@SenSys
- 2014
This work presents an approach for accurate occupancy estimation using a wireless sensor network that only collects non-sensitive data and a novel, hierarchical analysis method, and shows how the system is used for analysing historical data and identify effective room misuse and thus a potential for energy saving.
Real-time occupancy detection using decision trees with multiple sensor types
- Computer ScienceSpringSim
- 2011
This work used Decision Trees to perform the classification and to explore the relationship between different types of sensors, features derived from sensor data, and occupancy, and found that the individual feature which best distinguished presence from absence was the root mean square error of a passive infrared motion sensor, calculated over a two-minute period.
Occupancy detection through an extensive environmental sensor network in an open-plan office building
- Engineering
- 2009
A study to develop algorithms for occupancy number detection based on the analysis of environmental data captured from existing sensors and ambient sensing networks in the Robert L. Preger Intelligent Workplace at Carnegie Mellon University.