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Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design
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Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 79893

Special Issue Editors

Dr. Ravi Srinivasan

E-Mail Website
Guest Editor
M.E. Rinker, Sr. School of Building Construction Management, College of Design, Construction and Planning, University of Florida, Gainesville, FL 32611-5703, USA
Interests: building/urban energy modeling, simulation, and visualization
Dr. Mahabir Bhandari

E-Mail Website
Guest Editor
Building Technology Research and Integration Center (BTRIC), Oak Ridge National Laboratory, Oak Ridge, TN, USA
Interests: building energy modeling and calibration; building controls and integration; building envelope

Special Issue Information

Dear Colleagues,

Owing to advancements in computer design, in today’s world, there are two sensational trends that support sustainable building design and engineering, namely, real-time building energy prediction/big data analysis and technology revolution.

On one hand, traditional building energy modeling and simulation tools have been widely employed for prediction analysis. Coupled with empirical validation, the accuracy of these tools has been tested and improved. Among others, existing buildings have benefited from building energy big data analysis such as deep learning to understand the behavior of building systems and their occupants in order to effectively provide thermal comfort yet reduce overall energy use. Low-cost, low-energy sensor technologies coupled with wireless data transfer have enabled onsite building and occupant-related data acquisition and have proved to be effective tools to validate prediction as necessary.

On the other hand, the remarkable and timely evolution of technologies supporting building design, engineering, operation, and maintenance cannot be understated. Technologies such as advances in geographic information system (GIS) mapping technology, unmanned aerial vehicles (UAVs), and virtual/augmented reality (VR/AR) promise superior data acquisition and exploration. These technologies are not only here to stay but poised for exponential growth. UAV or drone technologies have been used in building construction and maintenance phases; for example, UAVs have been used as human safety systems during the building construction phase, and these can be integrated with thermal imaging systems to inspect thermal bridging effects that affect energy use during the building operation phase. Similarly, VR/AR technologies have been re-introduced to the building design and engineering paradigm, although these technologies were “tested” for sustainable design a decade ago, and they were found to be unaffordable and cumbersome to use.

This Special Issue “Building Energy Prediction/Big Data Analysis and Sustainable Design” invites authors to submit papers that explore the nexus between building energy and technology revolution. Topics may include but are not limited to the following:

  • Building energy modeling and simulation;
  • Machine learning and automated building(s) model development;
  • Building energy big data analysis;
  • Sensor technologies for building data acquisition;
  • Building outdoor/indoor air quality and comfort measurements;
  • Virtual reality (VR), augmented reality (AR) in sustainable design and engineering;
  • Urban energy modeling and simulation;
  • Urban energy visualization techniques;
  • Sustainability approaches for large campuses or urban systems.

Dr. Ravi Srinivasan
Dr. Mahabir Bhandari
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Building energy modeling
  • Machine learning
  • Automated building model development
  • Big data analysis
  • Sensor and control technologies
  • Virtual reality and augmented reality visualization
  • Urban energy modeling
  • Sustainability

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Published Papers (13 papers)

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Research

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15 pages, 3823 KiB  
Article
Low Cost, Multi-Pollutant Sensing System Using Raspberry Pi for Indoor Air Quality Monitoring
by He Zhang, Ravi Srinivasan and Vikram Ganesan
Sustainability 2021, 13(1), 370; https://doi.org/10.3390/su13010370 - 3 Jan 2021
Cited by 68 | Viewed by 10460
Abstract
Deteriorating levels of indoor air quality is a prominent environmental issue that results in long-lasting harmful effects on human health and wellbeing. A concurrent multi-parameter monitoring approach accounting for most crucial indoor pollutants is critical and essential. The challenges faced by existing conventional [...] Read more.
Deteriorating levels of indoor air quality is a prominent environmental issue that results in long-lasting harmful effects on human health and wellbeing. A concurrent multi-parameter monitoring approach accounting for most crucial indoor pollutants is critical and essential. The challenges faced by existing conventional equipment in measuring multiple real-time pollutant concentrations include high cost, limited deployability, and detectability of only select pollutants. The aim of this paper is to present a comprehensive indoor air quality monitoring system using a low-cost Raspberry Pi-based air quality sensor module. The custom-built system measures 10 indoor environmental conditions including pollutants: temperature, relative humidity, Particulate Matter (PM)2.5, PM10, Nitrogen dioxide (NO2), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Carbon dioxide (CO2), and Total Volatile Organic Compounds (TVOCs). A residential unit and an educational office building was selected and monitored over a span of seven days. The recorded mean PM2.5, and PM10 concentrations were significantly higher in the residential unit compared to the office building. The mean NO2, SO2, and TVOC concentrations were comparatively similar for both locations. Spearman rank-order analysis displayed a strong correlation between particulate matter and SO2 for both residential unit and the office building while the latter depicted strong temperature and humidity correlation with O3, SO2, PM2.5, and PM10 when compared to the former. Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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38 pages, 7943 KiB  
Article
Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses
by Kuldeep Kurte, Jeffrey Munk, Olivera Kotevska, Kadir Amasyali, Robert Smith, Evan McKee, Yan Du, Borui Cui, Teja Kuruganti and Helia Zandi
Sustainability 2020, 12(18), 7727; https://doi.org/10.3390/su12187727 - 18 Sep 2020
Cited by 39 | Viewed by 5690
Abstract
Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, more exploration is needed on understanding the adaptability [...] Read more.
Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, more exploration is needed on understanding the adaptability challenges that the DRL agent could face during the deployment phase. Using online learning for such applications is not realistic due to the long learning period and likely poor comfort control during the learning process. Alternatively, DRL can be pre-trained using a building model prior to deployment. However, developing an accurate building model for every house and deploying a pre-trained DRL model for HVAC control would not be cost-effective. In this study, we focus on evaluating the ability of DRL-based HVAC control to provide cost savings when pre-trained on one building model and deployed on different house models with varying user comforts. We observed around 30% of cost reduction by pre-trained model over baseline when validated in a simulation environment and achieved up to 21% cost reduction when deployed in the real house. This finding provides experimental evidence that the pre-trained DRL has the potential to adapt to different house environments and comfort settings. Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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20 pages, 2471 KiB  
Article
Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities
by Seung-Min Jung, Sungwoo Park, Seung-Won Jung and Eenjun Hwang
Sustainability 2020, 12(16), 6364; https://doi.org/10.3390/su12166364 - 7 Aug 2020
Cited by 60 | Viewed by 5069
Abstract
Monthly electric load forecasting is essential to efficiently operate urban power grids. Although diverse forecasting models based on artificial intelligence techniques have been proposed with good performance, they require sufficient datasets for training. In the case of monthly forecasting, because just one data [...] Read more.
Monthly electric load forecasting is essential to efficiently operate urban power grids. Although diverse forecasting models based on artificial intelligence techniques have been proposed with good performance, they require sufficient datasets for training. In the case of monthly forecasting, because just one data point is generated per month, it is not easy to collect sufficient data to construct models. This lack of data can be alleviated using transfer learning techniques. In this paper, we propose a novel monthly electric load forecasting scheme for a city or district based on transfer learning using similar data from other cities or districts. To do this, we collected the monthly electric load data from 25 districts in Seoul for five categories and various external data, such as calendar, population, and weather data. Then, based on the available data of the target city or district, we selected similar data from the collected datasets by calculating the Pearson correlation coefficient and constructed a forecasting model using the selected data. Lastly, we fine-tuned the model using the target data. To demonstrate the effectiveness of our model, we conducted an extensive comparison with other popular machine-learning techniques through various experiments. We report some of the results. Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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19 pages, 5148 KiB  
Article
Power Grid Simulation Testbed for Transactive Energy Management Systems
by Ozgur Ozmen, James Nutaro, Michael Starke, Jeffrey Munk, Larry Roberts, Xiao Kou, Piljae Im, Jin Dong, Fangxing Li, Teja Kuruganti and Helia Zandi
Sustainability 2020, 12(11), 4402; https://doi.org/10.3390/su12114402 - 28 May 2020
Cited by 5 | Viewed by 3282
Abstract
To effectively engage demand-side and distributed energy resources (DERs) for dynamically maintaining the electric power balance, the challenges of controlling and coordinating building equipment and DERs on a large scale must be overcome. Although several control techniques have been proposed in the literature, [...] Read more.
To effectively engage demand-side and distributed energy resources (DERs) for dynamically maintaining the electric power balance, the challenges of controlling and coordinating building equipment and DERs on a large scale must be overcome. Although several control techniques have been proposed in the literature, a significant obstacle to applying these techniques in practice is having access to an effective testing platform. Performing tests at scale using real equipment is impractical, so simulation offers the only viable route to developmental testing at scales of practical interest. Existing power-grid testbeds are unable to model individual residential end-use devices for developing detailed control formulations for responsive loads and DERs. Furthermore, they cannot simulate the control and communications at subminute timescales. To address these issues, this paper presents a novel power-grid simulation testbed for transactive energy management systems. Detailed models of primary home appliances (e.g., heating and cooling systems, water heaters, photovoltaic panels, energy storage systems) are provided to simulate realistic load behaviors in response to environmental parameters and control commands. The proposed testbed incorporates software as it will be deployed, and enables deployable software to interact with various building equipment models for end-to-end performance evaluation at scale. Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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13 pages, 1571 KiB  
Article
Building Performance Evaluation Using Coupled Simulation of EnergyPlus™ and an Occupant Behavior Model
by Mengda Jia and Ravi Srinivasan
Sustainability 2020, 12(10), 4086; https://doi.org/10.3390/su12104086 - 16 May 2020
Cited by 20 | Viewed by 4370
Abstract
Building energy simulation programs are used for optimal sizing of building systems to reduce excessive energy wastage. Such programs employ thermo-dynamic algorithms to estimate every aspect of the target building with a certain level of accuracy. Currently, almost all building simulation tools capture [...] Read more.
Building energy simulation programs are used for optimal sizing of building systems to reduce excessive energy wastage. Such programs employ thermo-dynamic algorithms to estimate every aspect of the target building with a certain level of accuracy. Currently, almost all building simulation tools capture static features of a building including the envelope, geometry, and Heating, Ventilation, and Air Conditioning (HVAC) systems, etc. However, building performance also relies on dynamic features such as occupants’ interactions with the building. Such interactions have not been fully implemented in building energy simulation tools, which potentially influences the comprehensiveness and accuracy of estimations. This paper discusses an information exchange mechanism via coupling of EnergyPlus™, a building energy simulation engine and PMFServ, an occupant behavior modeling tool, to alleviate this issue. The simulation process is conducted in Building Controls Virtual Testbed (BCVTB), a virtual simulation coupling tool that connects the two separate simulation engines on a time-step basis. This approach adds a critical dimension to the traditional building energy simulation programs to seamlessly integrate occupants’ interactions with building components to improve the modeling capability, thereby improving building performance evaluation. The results analysis of this paper reveals a need to consider metrics that measure different types of comfort for building occupants. Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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22 pages, 4118 KiB  
Article
AI-Based Campus Energy Use Prediction for Assessing the Effects of Climate Change
by Soheil Fathi, Ravi S. Srinivasan, Charles J. Kibert, Ruth L. Steiner and Emre Demirezen
Sustainability 2020, 12(8), 3223; https://doi.org/10.3390/su12083223 - 16 Apr 2020
Cited by 27 | Viewed by 8353
Abstract
In developed countries, buildings are involved in almost 50% of total energy use and 30% of global annual greenhouse gas emissions. The operational energy needs of buildings are highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and [...] Read more.
In developed countries, buildings are involved in almost 50% of total energy use and 30% of global annual greenhouse gas emissions. The operational energy needs of buildings are highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based energy modeling of buildings, Artificial Intelligence (AI) has the capability to provide faster and higher accuracy estimates, given buildings’ historic energy consumption data. Looking beyond individual building levels, forecasting building energy performance can help city and community managers have a better understanding of their future energy needs, and to plan for satisfying them more efficiently. Focusing at an urban scale, this research develops a campus energy use prediction tool for predicting the effects of long-term climate change on the energy performance of buildings using AI techniques. The tool comprises four steps: Data Collection, AI Development, Model Validation, and Model Implementation, and can predict the energy use of campus buildings with 90% accuracy. We have relied on energy use data of buildings situated in the University of Florida, Gainesville, Florida (FL). To study the impact of climate change, we have used climate properties of three future weather files of Gainesville, FL, developed by the North American Regional Climate Change Assessment Program (NARCCAP), represented based on their impact: median (year 2063), hottest (2057), and coldest (2041). Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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28 pages, 8885 KiB  
Article
Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea
by Mansu Kim, Sungwon Jung and Joo-won Kang
Sustainability 2020, 12(1), 109; https://doi.org/10.3390/su12010109 - 22 Dec 2019
Cited by 28 | Viewed by 3995
Abstract
When researching the energy consumption of residential buildings, it is becoming increasingly important to consider how residents use energy. With the advancement of computing power and data analysis techniques, it is now possible to analyze user information using big data techniques. Here, we [...] Read more.
When researching the energy consumption of residential buildings, it is becoming increasingly important to consider how residents use energy. With the advancement of computing power and data analysis techniques, it is now possible to analyze user information using big data techniques. Here, we endeavored to integrate user information with the physical characteristics of residential buildings to analyze how these elements impact energy consumption. Regression analysis was conducted to accurately identify the impact of each element on energy consumption. It was found that six elements were influential in all seasons: the number of exterior walls, housing direction, housing area, number of years occupied, number of household members, and the occupation of the household head. The elements that had an impact in each period were then derived. Based on the results of the regression analysis, input variables for the training of an artificial neural network (ANN) model were selected for each period, and residential energy consumption prediction models were implemented based on actual consumption. The elements identified as those affecting energy consumption, through regression analysis, can be used for implementing prediction models with advanced forms. This study is significant in that we derived influential elements from an integrative perspective. Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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17 pages, 5631 KiB  
Article
Effect of the Built Environment on Natural Ventilation in a Historical Environment: Case of the Walled City of Famagusta
by Aref Arfaei and Polat Hançer
Sustainability 2019, 11(21), 6043; https://doi.org/10.3390/su11216043 - 31 Oct 2019
Cited by 3 | Viewed by 4282
Abstract
Passive building is among the most important subjects in architecture today. The key factor in terms of a solution is related to the level of renewable energy in buildings. Natural ventilation is among the effective factors in indoor thermal comfort. Virtual simulations prepare [...] Read more.
Passive building is among the most important subjects in architecture today. The key factor in terms of a solution is related to the level of renewable energy in buildings. Natural ventilation is among the effective factors in indoor thermal comfort. Virtual simulations prepare a basis for reliable and fast result outcomes. Computer Fluid Dynamics (CFD) software is available thanks to advances in technology and mathematical calculation to simulate projects with any conditions. This paper presents thermal comfort reduction where, in the simulation, the closed environment is considered rather than the individual building with no surroundings. In order to reach the conclusion, a comparison between a single building simulation and two locations in the walled city of Famagusta in the Turkish Republic of Northern Cyprus, a historical settlement, is provided to illustrate the changes according to the closed environment conditions. According to the results, if energy consultants aim to present realistic energy data in order to upgrade the level of sustainability of buildings, it is important to consider the effect of the closed environment on natural ventilation in their calculation. Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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15 pages, 1810 KiB  
Article
Application of a Big Data Framework for Data Monitoring on a Smart Campus
by William Villegas-Ch, Jhoann Molina-Enriquez, Carlos Chicaiza-Tamayo, Iván Ortiz-Garcés and Sergio Luján-Mora
Sustainability 2019, 11(20), 5552; https://doi.org/10.3390/su11205552 - 9 Oct 2019
Cited by 32 | Viewed by 5474
Abstract
At present, university campuses integrate technologies such as the internet of things, cloud computing, and big data, among others, which provide support to the campus to improve their resource management processes and learning models. Integrating these technologies into a centralized environment allows for [...] Read more.
At present, university campuses integrate technologies such as the internet of things, cloud computing, and big data, among others, which provide support to the campus to improve their resource management processes and learning models. Integrating these technologies into a centralized environment allows for the creation of a controlled environment and, subsequently, an intelligent environment. These environments are ideal for generating new management methods that can solve problems of global interest, such as resource consumption. The integration of new technologies also allows for the focusing of its efforts on improving the quality of life of its inhabitants. However, the comfort and benefits of technology must be developed in a sustainable environment where there is harmony between people and nature. For this, it is necessary to improve the energy consumption of the smart campus, which is possible by constantly monitoring and analyzing the data to detect any anomaly in the system. This work integrates a big data framework capable of analyzing the data, regardless of its format, providing effective and efficient responses to each process. The method developed is generic, which allows for its application to be adequate in addressing the needs of any smart campus. Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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21 pages, 7545 KiB  
Article
Quantifying Impacts of Urban Microclimate on a Building Energy Consumption—A Case Study
by Jiying Liu, Mohammad Heidarinejad, Saber Khoshdel Nikkho, Nicholas W. Mattise and Jelena Srebric
Sustainability 2019, 11(18), 4921; https://doi.org/10.3390/su11184921 - 9 Sep 2019
Cited by 28 | Viewed by 3727
Abstract
This paper considered an actual neighborhood to quantify impacts of the local urban microclimate on energy consumption for an academic building in College Park, USA. Specifically, this study accounted for solar irradiances on building and ground surfaces to evaluate impacts of the local [...] Read more.
This paper considered an actual neighborhood to quantify impacts of the local urban microclimate on energy consumption for an academic building in College Park, USA. Specifically, this study accounted for solar irradiances on building and ground surfaces to evaluate impacts of the local convective heat transfer coefficient (CHTC), infiltration rate, and coefficient of performance (COP) on building cooling systems. Using computational fluid dynamics (CFD) allowed for the calculation of local temperature and velocity values and implementation of the local variables in the building energy simulation (BES) model. The discrepancies among the cases with different CHTCs showed slight influence of CHTCs on sensible load, in which the maximum variations existed 1.95% for sensible cooling load and 3.82% for sensible heating load. The COP analyses indicated windward wall and upstream roof are the best locations for the installation of these cooling systems. This study used adjusted infiltration rate values that take into account the local temperature and velocity. The results indicated the annual cooling and heating energy increased by 2.67% and decreased by 2.18%, respectively. Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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24 pages, 5549 KiB  
Article
Constructal Macroscale Thermodynamic Model of Spherical Urban Greenhouse Form with Double Thermal Envelope within Heat Currents
by Lazaros Mavromatidis
Sustainability 2019, 11(14), 3897; https://doi.org/10.3390/su11143897 - 17 Jul 2019
Cited by 16 | Viewed by 3162
Abstract
Urban agriculture is becoming a timely environmental friendly practice to strengthen cities’ resilience to climate change. However, there is a lack of academic literature regarding the thermodynamic potential of interior urban agriculture. Furthermore, there is always a need to develop, from scratch, an [...] Read more.
Urban agriculture is becoming a timely environmental friendly practice to strengthen cities’ resilience to climate change. However, there is a lack of academic literature regarding the thermodynamic potential of interior urban agriculture. Furthermore, there is always a need to develop, from scratch, an updated methodological approach that aims to assist architects of conceiving such specific thermodynamically complex interior environments. In this paper, urban space is identified as a ‘flow system’, and Bejan’s constructal law of generation of flow structure is used to morph and discover the system flow architecture that offers greater global performance (greater access to what flows). More precisely, a macroscale thermodynamic model of spherical urban greenhouse form with double thermal envelope has been developed while the methodological approach resulted in the definition of a decisional flowchart that can be reproduced by other researchers. On the basis of this macroscale constructal model, the present paper proposes reduced models that link thermodynamic and geometric parameters in an accurate manner and can be used at early design stages for pedagogic and qualitative optimization purposes, integrating urban farming to architectural programming. Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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18 pages, 7444 KiB  
Article
Occupant Behavior for Energy Conservation in Commercial Buildings: Lessons Learned from Competition at the Oak Ridge National Laboratory
by Amanda Ahl, Gina Accawi, Bryce Hudey, Melissa Lapsa and Teresa Nichols
Sustainability 2019, 11(12), 3297; https://doi.org/10.3390/su11123297 - 14 Jun 2019
Cited by 8 | Viewed by 3609
Abstract
Accompanying efforts worldwide to deploy sustainable building technologies shows a pressing need for expanded research on occupant behavior. Discourse is lacking concerning drivers of occupant behavior for energy conservation, especially in the case of commercial buildings. This paper explores potential determinants of occupant [...] Read more.
Accompanying efforts worldwide to deploy sustainable building technologies shows a pressing need for expanded research on occupant behavior. Discourse is lacking concerning drivers of occupant behavior for energy conservation, especially in the case of commercial buildings. This paper explores potential determinants of occupant behavior for energy conservation in commercial buildings. This is investigated in a case study of a two-month energy conservation competition involving eight office buildings at the Oak Ridge National Laboratory. Four buildings achieved energy savings based on the previous year’s baseline. Potential challenges and success factors of occupant behavior for energy conservation during the competition were explored based on an explanatory research design incorporating energy data, participant interviews, and surveys. The findings suggest that both social and technological aspects may be important drivers of energy conservation. The determinants of occupant behavior for energy conservation in commercial buildings suggested for further research include bottom-up involvement, stakeholder relationship management, targeted information, real-time energy visualization, and mobile social platforms. This paper presents initial implications, with a need for further research on these propositions and on their impacts on occupant behavior. This paper aims to contribute to both academia and practitioners in the arena of commercial building sustainability. Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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Review

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38 pages, 509 KiB  
Review
A Systematic Review of Air Quality Sensors, Guidelines, and Measurement Studies for Indoor Air Quality Management
by He Zhang and Ravi Srinivasan
Sustainability 2020, 12(21), 9045; https://doi.org/10.3390/su12219045 - 30 Oct 2020
Cited by 74 | Viewed by 17197
Abstract
The existence of indoor air pollutants—such as ozone, carbon monoxide, carbon dioxide, sulfur dioxide, nitrogen dioxide, particulate matter, and total volatile organic compounds—is evidently a critical issue for human health. Over the past decade, various international agencies have continually refined and updated the [...] Read more.
The existence of indoor air pollutants—such as ozone, carbon monoxide, carbon dioxide, sulfur dioxide, nitrogen dioxide, particulate matter, and total volatile organic compounds—is evidently a critical issue for human health. Over the past decade, various international agencies have continually refined and updated the quantitative air quality guidelines and standards in order to meet the requirements for indoor air quality management. This paper first provides a systematic review of the existing air quality guidelines and standards implemented by different agencies, which include the Ambient Air Quality Standards (NAAQS); the World Health Organization (WHO); the Occupational Safety and Health Administration (OSHA); the American Conference of Governmental Industrial Hygienists (ACGIH); the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE); the National Institute for Occupational Safety and Health (NIOSH); and the California ambient air quality standards (CAAQS). It then adds to this by providing a state-of-art review of the existing low-cost air quality sensor (LCAQS) technologies, and analyzes the corresponding specifications, such as the typical detection range, measurement tolerance or repeatability, data resolution, response time, supply current, and market price. Finally, it briefly reviews a sequence (array) of field measurement studies, which focuses on the technical measurement characteristics and their data analysis approaches. Full article
(This article belongs to the Special Issue Building and Urban Energy Prediction-Big Data Analysis and Sustainable Design)
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