107 research outputs found

    Predictive analytics in facilities management: A pilot study for exploring environmental comfort using wireless sensors

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    Purpose: Advancements in wireless sensor technology and building modelling techniques have enabled facilities managers to understand the environmental performance of the workplace in more depth than ever before. However, it is unclear to what extent this data can be used to predict subjective environmental comfort. Thus, the aim of this study was to pilot test a methodological framework for integrating real-time environmental data with subjective ratings of environmental comfort. Design/Methodology/Approach: An open-plan office was fitted with environmental sensors to measure key indoor environmental quality parameters (carbon dioxide, temperature, humidity, illumination, and sound pressure level). Additionally, building modelling techniques were used to calculate two spatial metrics (‘workspace integration’ and workspace density) for each workspace within the study area. 15 employees were repeatedly sampled across an 11-day study period, providing 78 momentary assessments of environmental comfort. Multilevel models were used to explore the extent to which the objective environmental data predicted subjective environmental comfort. Findings: Higher carbon dioxide levels were associated with more negative ratings of air quality, higher ‘workspace integration’ was associated with higher levels of distractions, and higher workspace density was associated with lower levels of social interactions. Originality/Value: To our knowledge, this is the first field study to directly explore the relationship between physical environment data collected using wireless sensors and subjective ratings of environmental comfort. The study provides proof-of-concept for a methodological framework for the integration of building analytics and human analytics

    Leveraging ubiquitous computing as a platform for collecting real-time occupant feedback in buildings

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    Building occupants represent a rich source of information for evaluating environmental design practices and building operations.  This paper presents a scalable diagnostic technology for collecting real-time Indoor Environmental Quality (IEQ) feedback from building occupants: an interactive desktop polling station. The device demonstrates the potential of ubiquitous computing, a model of human-computer interaction in which information processing is integrated into everyday objects, to engage occupants in providing IEQ feedback in real work environments.  Example data from a field study of a high-performance office building are presented demonstrating the applicability of multiple devices to acquire detailed feedback over daily and seasonal variations in climatic conditions.  Sample results show how polling station data can help identify the frequency and magnitude of discomfort with the spatial and temporal granularity needed to assess, validate, and improve the performance of environmentally responsive building technologies, controls, and design strategies. Analysis of repeated-measures subjective assessments paired with concurrent physical measurements is performed to demonstrate how existing standards and assumptions for occupant comfort could be evaluated and refined using detailed occupant feedback from buildings in use.  Results are discussed regarding implications for improving decision-making for the design, certification, and operation of environmentally responsive buildings

    A study of the impact of individual thermal control on user comfort in the workplace: Norwegian cellular vs. British open plan offices

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    In modern offices, user control is being replaced by centrally operated thermal systems, and in Scandinavia, personal offices by open plan layouts. This study examined the impact of user control on thermal comfort and satisfaction. It compared a workplace, which was designed entirely based on individual control over the thermal environment, to an environment that limited thermal control was provided as a secondary option for fine-tuning: Norwegian cellular and British open plan offices. The Norwegian approach provided each user with control over a window, door, blinds, heating and cooling as the main thermal control system. In contrast, the British practice provided a uniform thermal environment with limited openable windows and blinds to refine the thermal environment for occupants seated around the perimeter of the building. Field studies of thermal comfort were applied to measure users’ perception of thermal environment, empirical building performance and thermal control. The results showed a 30% higher satisfaction and 18% higher comfort level in the Norwegian offices compared to the British practices. However, the energy consumption of the Norwegian case studies was much higher compared to the British ones. A balance is required between energy efficiency and user thermal comfort in the workplace

    Summertime temperatures and thermal comfort in UK homes

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    This is an Author's Accepted Manuscript of an article published in Building Research and Information [copyright Taylor & Francis], available online at: http://www.tandfonline.com/10.1080/09613218.2013.757886Internal summertime temperatures measured in 268 homes in the UK city of Leicester are reported. The hourly data was collected from living rooms and bedrooms during the summer of 2009, which was generally cool but with a short hot spell. Some household interviews were conducted. The sample of homes is statistically representative of the socio-technical characteristics of the city’s housing stock. The data provides insight into the influence of house construction, energy system usage and occupant characteristics on the incidence of elevated temperatures and thermal discomfort. The warmest homes were amongst the 13% that were heated. Significantly more of these were occupied by those over 70 who are particularly vulnerable to high temperatures. The national heatwave plan might usefully caution against summertime heating. Temperatures in the 230 free-running homes were analysed using both static criteria and criteria associated with the BSEN15251 adaptive thermal comfort model. These indicated that that flats tended to be significantly warmer than other house types. Solid wall homes and detached houses tended to be significantly cooler. It is argued that adaptive criteria provide a valuable and credible framework for assessing internal temperatures in free-running UK homes. However, the temperatures in the Leicester homes were much lower than anticipated by the BSEN15251 model. Numerous possible reasons for this discrepancy are discussed

    A novel clustering-enhanced adaptive artificial neural network model for predicting day-ahead building cooling demand

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    To accurately predict hourly day-ahead building cooling demand, year-round historical weather profile needs to be evaluated. The daily weather profiles among different time periods result in various features of historical datasets. The different appropriate structure and parameters of artificial neural network models may be identified for training datasets with different features. In this study, a novel clustering-enhanced adaptive artificial neural network (C-ANN) model is proposed to forecast 24h-ahead building cooling demand in subtropical areas. The uniqueness of the proposed adaptive model is that k-means clustering is implemented to recognise representative patterns of daily weather profile and thus categorize the annual datasets into featuring clusters. Each cluster of the weather profile, along with the corresponding time variables and cooling demand, is adopted to train one ANN sub-model. The optimal structure and parameters of each ANN sub-model are selected according to its featuring training datasets; thus the ANN sub-models are adaptive. The proposed C-ANN model is tested on a representative office building in Hong Kong. It is found that the mean absolute percentage error of the training and testing cases of the proposed predictive model is 3.59% and 4.71%, which has 4.2% and 3.1% improvement compared to conventional ANN model with a fixed structure. The proposed adaptive predictive model can be applied in building energy management system to accurately predict day-ahead building cooling demand using the latest forecast weather profile

    A study of indoor carbon dioxide levels and sick leave among office workers

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    BACKGROUND: A previous observational study detected a strong positive relationship between sick leave absences and carbon dioxide (CO(2)) concentrations in office buildings in the Boston area. The authors speculated that the observed association was due to a causal effect associated with low dilution ventilation, perhaps increased airborne transmission of respiratory infections. This study was undertaken to explore this association. METHODS: We conducted an intervention study of indoor CO(2) levels and sick leave among hourly office workers employed by a large corporation. Outdoor air supply rates were adjusted periodically to increase the range of CO(2) concentrations. We recorded indoor CO(2) concentrations every 10 minutes and calculated a CO(2) concentration differential as a measure of outdoor air supply per person by subtracting the 1–3 a.m. average CO(2) concentration from the same-day 9 a.m. – 5 a.m. average concentration. The metric of CO(2) differential was used as a surrogate for the concentration of exhaled breath and for potential exposure to human source airborne respiratory pathogens. RESULTS: The weekly mean, workday, CO(2) concentration differential ranged from 37 to 250 ppm with a peak CO(2) concentration above background of 312 ppm as compared with the American Society of Heating, Refrigeration and Air-conditioning Engineers (ASHRAE) recommended maximum differential of 700 ppm. We determined the frequency of sick leave among 294 hourly workers scheduled to work approximately 49,804.2 days in the study areas using company records. We found no association between sick leave and CO(2) differential CONCLUSIONS: The CO(2) differential was in the range of very low values, as compared with the ASHRAE recommended maximum differential of 700 ppm. Although no effect was found, this study was unable to test whether higher CO(2) differentials may be associated with increased sick leave

    The Adaptive Thermal Comfort Review from the 1920s, the Present, and the Future

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    The typical method for comfort analysis is the Predicted Mean Vote and Predicted Percentage Dissatisfied (PMV-PPD). However, they present limitations in accommodating the comfort of a disabled and elder group of people, which are the most vulnerable to climate change and energy poverty. The adaptive method can give flexibility and personalisation needed to overcome the problem due to the variability of the people's metabolism, historical and behavioural preferences. Investments to upgrade the indoor environmental quality and building design can then be effectively used and, for the first time, it will be possible to tailor the solutions for these particular groups of people. The adaptive approach uses Artificial Intelligence (AI), where it can introduce the imperfect learning process. Overcoming this, instead of going further for the Explainable AI, the PMV–PPD approach can be used for the learning validation and verification needed for the adaptive setting point and standards
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