IoT-based Occupancy Estimation Models for Indoor Non-Residential Environments
01 Dec 2019-
TL;DR: In this paper, an IoT end-to-end system was developed to collect relative humidity (RH), CO 2 concentration and occupant count of a University classroom, and the RH and CO 2 data has been used to compute estimates of student occupancy using regression based estimation models.
Abstract: An IoT end to end system has been developed in this work to collect relative humidity (RH), CO 2 concentration and occupant count of a University classroom. The RH and CO 2 data has been used to compute estimates of student occupancy using regression based estimation models. Multiple linear and quantile regression models have been explored for occupancy estimation by using RH, CO 2 , and both RH as well as CO 2 concentration respectively. The estimation performance of these models has been compared by using mean absolute percentage error. The quantile regression based models have been found to be the most accurate with a mean absolute percentage error of 2.47%.
Citations
More filters
[...]
TL;DR: In this article , an adaptive neuro-fuzzy inference system (ANFIS) and discrete-time Markov chains (DTMC) were used to predict the state of the indoor environment with the help of daily air pollution concentrations and environmental parameters.
Abstract: • An IoT-based framework for data collection and context modelling has been developed. • Extended Kalman Filter (EKF) approach is used to deal with inaccuracies, missing data and eliminate errors. • The EKF-derived indoor pollutant concentrations are employed in context reasoning for proposing a new index. • An adaptive neuro-fuzzy inference system (ANFIS) uses the percent of dissatisfied people (PPD), ventilation rate (VR), and AQI data to identify the present state of IAQ and the percentage of time when the air quality in the classroom is unhealthy. For a productive and healthy life, air quality plays an important role. This paper addresses the requirements to develop a system capable of providing real-time information, predictions, and alerts about the indoor environment using context-awareness. The proposed IoT system serves for data collection, pre-processing, defining rules, and forecasting the predicting states of the indoor environment by giving information to the end-user about the alerts and recommendations. A novel approach based on the indoor pollutants T, RH, CO 2 , PM 2.5 , PM 10 , and CO for the determination of the status of the environment is proposed. The pre-processing is used for filtering data using and extended Kalman filter. Further, the system uses an adaptive neuro-fuzzy inference system (ANFIS) and discrete-time Markov chains (DTMC) to predict the state of the indoor environment with the help of daily air pollution concentrations and environmental parameters. The ANFIS model predictor considers the value of indoor pollutants to form a new index: State of indoor air (SIA). For analysis and forecasting of the new index SIA, the DTMC model is used. The collected and measured data is stored in the IoT cloud using the sensing setup, and sensed information is used to develop the SIA transfer matrix, generating return durations corresponding to each SIA and providing alerts based on the data to the end-user. The models are assessed using the expected and actual return durations. The most frequent interior ventilation states, according to the predictions, are poor and moderate. Only 0.08 percent of the time does the IAQ remain in a good state. Two-thirds of the time (66%), the indoor ventilation is severe (poor, very poor, or hazardous); 19% of the time it is very bad, and 15% of the time it is hazardous, suggesting and warning that there is a very high probability of unhealthy AQI in educational institutions in the Delhi-NCR region. Performance is measured by the comparison between actual and forecasted return periods, and the forecast error for our system is low, with an absolute forecast error of 3.47% on an average.
1 citations
[...]
TL;DR: In this paper , a survey explores the use of sensors in smart building environments, identifying different approaches to employ sensors in buildings and the most commonly used data-driven approaches for activity recognition in such buildings.
Abstract: Increasingly, buildings are being fitted with sensors for the needs of different sectors, such as education, industry and business. Using Internet of Things (IoT) devices combined with analysis of data being generated by these devices, it is possible to infer a number of metrics, e.g. building occupancy and activities of occupants. The information thus gathered can be used to develop software applications to support energy management, occupant comfort, and space utilization. This survey explores the use of sensors in smart building environments, identifying different approaches to employ sensors in buildings. The most commonly used data-driven approaches for activity recognition in such buildings is also investigated, concluding by highlighting current research challenges and future research directions in this area.
References
More filters
[...]
910 citations
[...]
01 Jan 2009
442 citations
"IoT-based Occupancy Estimation Mode..." refers background in this paper
[...]
Book•
[...]
28 Apr 2009
TL;DR: This book is especially written for graduate students and research engineers who work on noise reduction for speech and audio applications and want to understand the subtle mechanisms behind each approach.
Abstract: Noise is everywhere and in most applications that are related to audio and speech, such as human-machine interfaces, hands-free communications, voice over IP (VoIP), hearing aids, teleconferencing/telepresence/telecollaboration systems, and so many others, the signal of interest (usually speech) that is picked up by a microphone is generally contaminated by noise. As a result, the microphone signal has to be cleaned up with digital signal processing tools before it is stored, analyzed, transmitted, or played out. This cleaning process is often called noise reduction and this topic has attracted a considerable amount of research and engineering attention for several decades. One of the objectives of this book is to present in a common framework an overview of the state of the art of noise reduction algorithms in the single-channel (one microphone) case. The focus is on the most useful approaches, i.e., filtering techniques (in different domains) and spectral enhancement methods. The other objective of Noise Reduction in Speech Processing is to derive all these well-known techniques in a rigorous way and prove many fundamental and intuitive results often taken for granted. This book is especially written for graduate students and research engineers who work on noise reduction for speech and audio applications and want to understand the subtle mechanisms behind each approach. Many new and interesting concepts are presented in this text that we hope the readers will find useful and inspiring.
355 citations
[...]
TL;DR: In this paper, the authors investigated the potential of using occupancy information to realize a more energy efficient building climate control, focusing on Swiss office buildings equipped with Integrated Room Automation (IRA), i.e. the integrated control of Heating, Ventilation, Air Conditioning (HVAC) as well as lighting and blind positioning of a building zone or room.
Abstract: This paper investigates the potential of using occupancy information to realize a more energy efficient building climate control. The study focuses on Swiss office buildings equipped with Integrated Room Automation (IRA), i.e. the integrated control of Heating, Ventilation, Air Conditioning (HVAC) as well as lighting and blind positioning of a building zone or room. To evaluate the energy savings potential, different types of occupancy information are used in a Model Predictive Control (MPC) framework, which is well-suited for this study due to its ability to readily include occupancy information in the control.
An MPC controller, which controls the building based on a standard fixed occupancy schedule, is used as a benchmark. The energy use of this benchmark is compared with three other control strategies: first, the same MPC controller which uses the same schedule for control as the benchmark, but turns off the lighting in case of (an instantaneous measurement of) vacancy; second, the same MPC controller which uses the same schedule as the benchmark for control, but turns off lighting and ventilation in case of (an instantaneous measurement of) vacancy; and third, the same MPC controller as the benchmark but using a perfect prediction about the upcoming occupancy.
This comparison is carried out for different buildings, HVAC systems, seasons and occupancy patterns in order to determine their influence on the energy savings potential.
298 citations
"IoT-based Occupancy Estimation Mode..." refers background in this paper
[...]
[...]
[...]
[...]
[...]
[...]
TL;DR: In this paper, the authors present a review of the literature published in leading journals through Science Direct and Scopus databases within this research domain to establish research trends, and importantly, to identify research gaps for future investigation.
Abstract: Over the past 15 years, the evaluation of energy demand and use in buildings has become increasingly acute due to growing scientific and political pressure around the world in response to climate change. The estimation of the use of energy in buildings is therefore a critical process during the design stage. This paper presents a review of the literature published in leading journals through Science Direct and Scopus databases within this research domain to establish research trends, and importantly, to identify research gaps for future investigation. It has been widely acknowledged in the literature that there is an alarming performance gap between the predicted and actual energy consumption of buildings (sometimes this has been up to 300% difference). Analysis of the impact of occupants’ behaviour has been largely overlooked in building energy performance analysis. In short, energy simulation tools utilise climatic data and physical/ thermal properties of building elements in their calculations, and the impact of occupants is only considered through means of fixed and scheduled patterns of behaviour. This research review identified a number of areas for future research including: larger scale analysis (e.g. urban analysis); interior design, in terms of space layout, and fixtures and fittings on occupants’ behaviour; psychological cognitive behavioural methods; and the integration of quantitative and qualitative research findings in energy simulation tools to name but a few.
241 citations
"IoT-based Occupancy Estimation Mode..." refers background in this paper
[...]
Related Papers (5)
[...]