• Energy Research
• 11. Sustainability close
• 2. Zero hunger close
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Abstract Previous studies indicate electricity consumption is increasing in new and green buildings highlighting the importance of investigating parameters affecting that increase. The majority of previous studies also focused on studying commercial or residential buildings emphasizing the need to study energy consumption in other building types. This study analyzed historical energy consumption data in a representative sample of thirty schools in Manitoba, Canada. It showed that the decrease in gas consumption for heating in new schools was counteracted by a statistically significant increase in their electricity consumption. Three cases study schools were selected for further analysis of their electricity consumption. Within each school, one classroom, the gymnasium, as well as spaces with significant community use, were sub-metered to collect real-time electricity consumption data. Results indicated total electricity consumption increased in the newest school, although sub-metered spaces in older schools consumed more electricity. Variations in electricity consumption between sub-metered spaces were attributed to occupant behaviour. The study is the first to provide an in-depth investigation of electricity consumption in Canadian school buildings and consider the potential effect of typically overlooked parameters such as occupant behaviour on their overall energy consumption.

Abstract Cool materials have been proposed for building applications given their potentialities in reducing building energy consumption, urban heat island effects, and global warming. Among cool materials, retro-reflective (RR) ones have been recently proposed for their ability to reflect backwards the incidental striking solar energy. This property is useful in densely urbanized areas, in urban canyons patterns, and in urban areas with buildings of different heights, because it avoids that the reflected energy contributes to the overheating of the neighbor buildings and structures. This study aims to predict the long-term performance of some RR tiles and paints intended for building applications. To this purpose, laboratory accelerated aging tests as described in the ASTM G154 were performed to determine the long-term material properties within a much shorter time than with outdoor weather aging. The samples show minimal changes in the reflectivity, directional reflection, and colorimetry. Finally, this paper shows that the RR character of the investigated materials and their urban cooling potential would be preserved.

Extensive research works have been carried out over the past few decades in the development of simulation tools to predict the thermal performance of buildings. These validated tools have been used in the design of the building and its components. However, limited simulation tools have been developed for modeling of district energy systems, which can potentially be a very laborious and time-consuming process. Besides many associated limitations, providing a realistic demand profile of the district energy systems is not a straightforward task due to high number of parameters involved in predicting a detail demand profile.This paper reports the development of a simplified model for predicting the thermal demand profile of a district heating system. The paper describes the method used to develop two types of simplified models to predict the thermal load of a variety of buildings (residential, office, attached, detached, etc.). The predictions were also compared with those made by the detailed simulation models.The simplified model was then utilized to predict the energy demand of a variety of districts types (residential, commercial or mix), and its prediction accuracy was compared with those made by detailed model: good agreement was observed between the results.

Abstract Building codes require domestic hot water supplied to high-rise residential buildings to be stored at 60∘C or above to avoid bacterial contamination (e.g., Legionella), while the supplied water delivered to residential units should not exceed 49∘C to avoid scalding. Inefficient mixing of hot and cold water to lower the water temperature can cause temperature and pressure fluctuations of supplied water resulting in resident discomfort, higher energy consumption and risk to plumbing fixture. This work compares the efficiency of two mixing devices (a) an electronic mixing valve (EMV) and (b) a thermostatic mixing valve (TMV) experimentally and numerically. The energy savings, pressure drop, and temperature fluctuation caused by each device in the same building are presented. Each 24–hour period is divided into 4 time periods based on hot water demand. The results show that the EMV can provide steadier temperature during the transition between low demand period overnight and peak consumption in the early morning, and lower pressure drop in the hot water system. In addition, depending on the circulation flowrate in the building, the EMV can potentially reduce energy consumption by more than 40% particularly during low demand periods.

Abstract The energy consumption of commercial buildings in Montreal (Canada) with absorptive and reflective skin materials is investigated. Four building prototypes (small office, medium office, large office, and retail store) with two types of heating systems (gas heating and heat pump) categorized based on vintage and HVAC system (old building with old HVAC, old building with new HVAC, and new building with new HVAC) are studied (total of 24 cases). For each case three simulations are performed using DOE-2 building energy model: (1) dark roof and walls with measured weather data: representing the actual case and the basis for comparing the effect on albedo enhancement, (2) cool roof and walls with measured weather data: determining the direct effect of increasing albedo of an individual building, and (3) cool roof and walls with modified weather data: predicting the effect of albedo enhancement in urban scale that contributes to weather condition. In all cases the effect of snow on the roof is considered and for modification of the weather data output of mesoscale meteorological simulation is used. Calculated energy consumption and energy expenditure showed that, in general, increasing the reflectivity of building skin saves energy and money. The surface modification is more beneficial for old vintage buildings. The maximum expenditure savings is about 11% for buildings with dark roof and walls, where the contribution of urban-scale increase in the surface albedo can be as high as 4% in the total energy expenditure savings.

Abstract Statistical models can be used as surrogates of detailed simulation models. Their key advantage is that they are evaluated at low computational cost which can remove computational barriers in building performance simulation. This comprehensive review discusses significant publications in sustainable building design research where surrogate modelling was applied. First, we familiarize the reader with the field and begin by explaining the use of surrogate modelling for building design with regard to applications in the conceptual design stage, for sensitivity and uncertainty analysis, and for building design optimisation. This is complemented with practical instructions on the steps required to derive a surrogate model. Next, publications in the field are discussed and significant methodological findings highlighted. We have aggregated 57 studies in a comprehensive table with details on objective, sampling strategy and surrogate model type. Based on the literature major research trends are extracted and useful practical aspects outlined. As surrogate modelling may contribute to many sustainable building design problems, this review summarizes and aggregates past successes, and serves as practical guide to make surrogate modelling accessible for future researchers.

Abstract In the Canadian residential sector, the end-uses of appliances, lighting, and plug loads (ALP) account for 16% of total end-use energy consumption. In an effort to reduce the impacts of this energy consumption, electricity technologies such as solar photovoltaics and smart appliances are being adopted. Evaluation of their performance requires an understanding of residential electricity use patterns. Building simulation tools can estimate the time-step performance of such technologies, but require accurate and representative ALP electricity profiles as an input. Sub-metered datasets lack in quantity and thus overall representativeness of the sector. Meanwhile, large, representative datasets are becoming available through electricity smart-metering programs, but usually consist only of whole-house electricity load and lack summary household characteristics (e.g. occupancy, floor space, appliance descriptions). However, homes which are not electrically heated (space, water) or cooled may function as ALP load profiles for simulation. This research addresses these loads with a new method of distinguishing non-electrically heated and cooled homes from a broad dataset of whole-house profiles. The method originates from a comparison of two electricity load datasets: (i) “smart-meter” 15-min time-step whole-house data for 160 homes spanning three years, and (ii) “sub-metered” 1-min time-step data for 23 residential homes. This comparison also speaks to the usefulness of whole-house electricity smart-meter information to building performance simulation.

Abstract Identifying the impacts of occupants on building energy consumption has become an important issue in recent years. This is due to the interrelationship of influencing factors such as urban climate, building characteristics, occupant behavior, and building services and operation, which makes it challenging to identify the role of occupants in energy consumption. The research problem in this study lies in the fact that the occupants of a building may not be cautious regarding energy savings, and there exists no ground to assess their energy consumption behavior. One solution is the development of a systematic comparison procedure between similar buildings. This paper introduces a new procedure for comparison between occupants of several buildings to show the rank of each building among others and suggest occupants on reducing their energy consumption and improving their rank. The proposed framework is developed based on multiple data-mining methods, including clustering, association rules mining, and neural networks. The proposed methodology is composed of two levels. The first considers the amount of energy usage by occupants after filtering effects unrelated to the occupant behavior. The second ranks the buildings in terms of achieved and potential savings during the time under investigation. To demonstrate the application, the methodology was applied on a set of monitored residential buildings in Japan. Results suggest that the proposed method enhances the evaluation of buildings’ energy-saving potential by revealing the occupants’ contribution. It also provides diverse and prioritized strategies to help occupants manage their energy consumption by revealing the building energy end-use patterns.

Abstract This paper presents optimization technique of residential and commercial buildings mixture within a mixed-use neighborhood, for enhanced energy performance and less GHG emission. The study, conducted for a Northern high latitude location, investigates several parameters related to building types, their density, floor area as well as ratio of specific building types to the total built area. Several methods are employed in the investigation, including energy simulations, statistical methods, optimization and decision making. A selection method of single optimal neighborhood scenario, based on the analysis of seasonal average hourly load is proposed. The results show that an optimal commercial to built land area ranges between 22% and 32%, with the remaining area dedicated to residential buildings. Multiple combinations of residential buildings that enhance energy performance while reducing GHG emissions are formed by 50% of detached houses and 50% of combined townhouses and apartment units (with different ratios). On the commercial side, near optimal combinations of commercial buildings area is constituted by 46–75% offices, 18–45% retails, and 6–9% supermarkets. Optimizing the hourly energy load profile, a scenario combining commercial land area of 25% with equal composition of office and retail buildings (47.5%), to residential area consisting of equal proportion of single detached and townhouses (48% each) can be selected.