Occupancy and indoor environment quality sensing for smart buildings

Han, Zhenyu; Gao, Robert X.; Fan, Zhaoyan

doi:10.1109/i2mtc.2012.6229557

Cited by 76 publications

(48 citation statements)

References 11 publications

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“…Time series models have been applied to many cases of outdoor environment 74,75 and building energy studies. 35 In indoor environments, time series models have also been applied to forecast temperature, 76 relative humidity, 76,77 CO 2 concentration, [76][77][78] and CO concentration. 78 The four parameters can be measured continuously using low-cost online sensors.…”

Section: Time Series Modelsmentioning

confidence: 99%

Machine learning and statistical models for predicting indoor air quality

et al. 2019

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Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed.Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM 2.5 and PM 10 ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees. K E Y W O R D Sartificial neural networks, data mining, IAQ, partial least squares, particulate matter, regression | 705 WEI Et al.

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Section: Time Series Modelsmentioning

confidence: 99%

Machine learning and statistical models for predicting indoor air quality

et al. 2019

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“…Examples of additional algorithms are discussed in [34] and [35]. In [36], an Autoregressive Hidden Markov Model (ARHMM) was developed to model the occupancy pattern using data from PIR sensors, carbon dioxide, concentration and relative humidity sensors. It was found that the algorithm has an average estimation accuracy of 80.78% and outperforms the other previously mentioned methods.…”

Section: Algorithms For Occupancy Detectionmentioning

confidence: 99%

A Web-Based Office Climate Control System Using Wireless Sensors

et al. 2016

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Abstract-Conventional heating, ventilating, and air conditioning (HVAC) systems usually achieve the desired control level by means of simple ON-OFF control, which can often result in high energy wastage. A potential solution to this issue is intelligent self-regulating HVAC controllers, which base their actions/decisions on sensor data. In this paper, an office climate monitoring and control system is designed and implemented. The system consists of various wireless sensor nodes and a control node. The sensor nodes provide the sensor data necessary to determine occupancy and the control node executes the algorithm, which decides whether to activate cooling or heating based on the sensor data. This system can serve as a controller and can be integrated into HVAC systems in smart buildings. It is shown that the developed control algorithm executed on the control node results in an improvement of up to 39% in energy efficiency over conventional ON-OFF controllers for HVAC systems.

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“…In summary, the EM-algorithm iterates between the E-Step to compute the sufficient statistics P(q t = i, q t+1 = j|Y ) and P(q t = i|Y ) with Equations (18), (19), (20), and (21) while fixing the parameters in (17a)−(17d), and the M-Step to update the parameters in (17a)−(17d) while fixing the sufficient statistics until some convergence criterion on the expected value of (15) is reached. .…”

Section: Parameter Estimation and Inferencementioning

confidence: 99%

A Bayesian perspective on Residential Demand Response using smart meter data

Zhou

Balandat

Tomlin

2016

2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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The widespread deployment of Advanced Metering Infrastructure has made granular data of residential electricity consumption available on a large scale. Smart meters enable a two-way communication between residential customers and utilities. One field of research that relies on such granular consumption data is Residential Demand Response, where individual users are incentivized to temporarily reduce their consumption during periods of high marginal cost of electricity. To quantify the economic potential of Residential Demand Response, it is important to estimate the reductions during Demand Response hours, taking into account the heterogeneity of electricity users. In this paper, we incorporate latent variables representing behavioral archetypes of electricity users into the process of short-term load forecasting with Machine Learning methods, thereby differentiating between varying levels of energy consumption. The latent variables are constructed by fitting Conditional Mixture Models of Linear Regressions and Hidden Markov Models on smart meter readings of a Residential Demand Response program in the western United States. We observe a notable increase in the accuracy of short-term load forecasts compared to the case without latent variables. We then estimate the reductions during Demand Response events conditional on the latent variables, and discover a higher DR reduction among users with automated smart home devices compared to those without.

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Occupancy and indoor environment quality sensing for smart buildings

Cited by 76 publications

References 11 publications

Machine learning and statistical models for predicting indoor air quality

Machine learning and statistical models for predicting indoor air quality

A Web-Based Office Climate Control System Using Wireless Sensors

A Bayesian perspective on Residential Demand Response using smart meter data

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