A probabilistic prediction model for window opening during transition seasons in office building

Window operation of occupants in building has close relationship with indoor air quality, indoor thermal environment and building energy performance. The objective of this study was to understand occupants' interaction with window opening in transition seasons considering the influence of subject type (e.g. active and passive respondents) and to develop corresponding predictive models. An investigation was carried out in non-air-conditioned building in the UK covering the period from September to November. Outdoor temperature in this study was determined as good predictor for window operation. The differences in window opening probabilities between active and passive subjects were significant. Active occupants preferred to open window for fresh air or for indoor thermal condition adjustment, even though the outdoor air temperature sometimes were less than 12 °C. Proper utilization of windows in transition seasons contributed significantly to building energy saving and further improve energy efficiency in buildings

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

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

Glazing daylighting performance and Trombe wall thermal performance of a modular façade system in four different Portuguese cities

This paper reports on a new façade system that uses passive solutions in the search for energy efficiency. The differentials are the versatility and flexibility of the modules, which are important advantages of the system. The thermal performance of Trombe walls and glazings and the daylighting performance of glazing were the key aspects analyzed in the results. Computational simulations were accomplished for the thermal performance of different arrangements of the modules with DesignBuilder software. The glazing daylighting performance was studied by means of Ecotect and Desktop Radiance programs and compared with the transmittance curves of glazings. Occupancy profile and internal gains were fixed according to the Portuguese reality for both studies. The main characteristics considered in this research were the use of two double glazings, four different climates in Portugal and one and two Trombe walls in the façade. The results show an important reduction in the energy consumption with the use of Trombe walls and double self-cleaning glazing in the façade, which also presented better daylighting performance.Author Helenice M Sacht benefited from a scholarship granted by Erasmus Mundus ISAC - Improving Skills Across Continents to perform her research work at University of Minho, from which resulted this article

Summertime temperatures and thermal comfort in UK homes

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

The heat is on: Effects of synchronous music on psychophysiological parameters and running performance in hot and humid conditions

© 2018 Nikol, Kuan, Ong, Chang and Terry. Running in high heat and humidity increases psychophysiological strain, which typically impairs running performance. Listening to synchronous music has been shown to provide psychophysiological benefits, which may enhance running performance. The present randomized, crossover study examined effects of listening to synchronous music on psychophysiological parameters and running performance in hot and humid conditions. Twelve male runners (21.7 ± 2.2 y; 166.17 ± 7.18 cm; 60.32 ± 9.52 kg; 59.29 ± 5.95 ml kg-1 min-1) completed two running trials in simulated conditions (31°C and 70% humidity) with and without synchronous music. Participants ran on a treadmill inside a climatic chamber for 60 min at 60% V.O2max and continued to run to exhaustion at 80% V.O2max. Time-to-exhaustion under the synchronous music condition was 66.59% longer (mean = 376.5 s vs. 226.0 s, p = 0.02, d = 0.63) compared to the no music condition. Ratings of perceived exertion were significantly lower for the synchronous music condition at each time point (15, 30, 45, and 60 min) of the steady state portion of the running trials. Small differences in heart rate were detected between conditions. No significant between-condition differences were found in urine specific gravity, percentage of body weight loss, thermal comfort, and blood lactate. Findings suggest that listening to synchronous music is beneficial to running performance and perceived exertion in hot and humid conditions.This present study was supported by Short-term Grant (USM304.PPSP.61313041) and Research University’s Individual Grant (1001/PPSP/812149) from the Universiti Sains Malaysia

The public health benefits of insulation retrofits in existing housing in the United States

BACKGROUND: Methodological limitations make it difficult to quantify the public health benefits of energy efficiency programs. To address this issue, we developed a risk-based model to estimate the health benefits associated with marginal energy usage reductions and applied the model to a hypothetical case study of insulation retrofits in single-family homes in the United States. METHODS: We modeled energy savings with a regression model that extrapolated findings from an energy simulation program. Reductions of fine particulate matter (PM(2.5)) emissions and particle precursors (SO(2 )and NOx) were quantified using fuel-specific emission factors and marginal electricity analyses. Estimates of population exposure per unit emissions, varying by location and source type, were extrapolated from past dispersion model runs. Concentration-response functions for morbidity and mortality from PM(2.5 )were derived from the epidemiological literature, and economic values were assigned to health outcomes based on willingness to pay studies. RESULTS: In total, the insulation retrofits would save 800 TBTU (8 × 10(14 )British Thermal Units) per year across 46 million homes, resulting in 3,100 fewer tons of PM(2.5), 100,000 fewer tons of NOx, and 190,000 fewer tons of SO(2 )per year. These emission reductions are associated with outcomes including 240 fewer deaths, 6,500 fewer asthma attacks, and 110,000 fewer restricted activity days per year. At a state level, the health benefits per unit energy savings vary by an order of magnitude, illustrating that multiple factors (including population patterns and energy sources) influence health benefit estimates. The health benefits correspond to $1.3 billion per year in externalities averted, compared with$5.9 billion per year in economic savings. CONCLUSION: In spite of significant uncertainties related to the interpretation of PM(2.5 )health effects and other dimensions of the model, our analysis demonstrates that a risk-based methodology is viable for national-level energy efficiency programs

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

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

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

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

Support for the integration of green roof constructions within Chinese building energy performance policies

Green roofs could act as a thermal buffer in buildings and offer potential energy savings. However, the energy benefits from green roofs are not usually properly recognised by traditional building energy regulations. Building energy regulations are traditionally over-simplistic during the assessment of the energy performance of complex building constructions. In the case of green roof designs, it is essential that the assessment mechanisms should not ignore the complex heat and moisture balances within the green roof layers. In this paper, dynamic energy modelling that considers the complexity within the green roof layers is adopted to guide policy makers in China on the relationship between using specific thicknesses of roof insulation against green roof layers. Simulations are run for a residential building type by also considering different thermal envelope characteristics across eight large Chinese cities and within the five main climatic zones of China. Results that link the green roof characteristics with respective traditional insulation layers are produced for all cities and it is found that optimising the plant and soil characteristics of green roofs in some climates could substitute more than 125 mm of roof insulation, while less optimum green roof types could only replace about 25 mm of roof insulation