• Energy Research

Abstract Sustainable building performance requires the integration of various metrics such as energy consumption, thermal comfort levels, occupants’ wellbeing, and productivity. Despite their interdependence, these metrics have been mostly evaluated independently, overlooking potential tradeoffs that can occur between them (e.g., energy conservation efforts and thermal comfort). In addition, human-related factors such as occupants’ energy consumption behaviors, schedules, and movements between buildings cannot be captured using current commercial building modeling tools. Consequently, simulating the performance of a group of buildings such as in a campus, neighborhood, or city remains very challenging. In this paper, a comprehensive agent-based modeling (ABM) framework is developed to: (1) model an urban area with several buildings along with the movements and actions of people within the environment; (2) calculate key performance metrics such as indoor/outdoor thermal comfort and energy consumption levels; and (3) test and propose strategies to optimize sustainable building operation. This study illustrates the multidisciplinary approach needed to capture various dimensions of sustainable building performance. The framework is then applied to a green campus environment, identifying an energy management strategy that can reduce energy consumption by 19% without compromising occupants’ comfort and wellbeing.

AbstractImportant energy reductions can be achieved in the building sector by providing occupants with feedback about their energy-consumption levels. Recent studies link the success of energy-feed...

Abstract Significant energy savings can be achieved in buildings by altering how occupants use and operate various building systems. The first step to successfully inducing such change requires a thorough assessment and understanding of the actual drivers and motivators of existing behaviors. The current literature on energy conservation behaviors in buildings presents significant limitations including: (1) the availability of data on human behavior in buildings; (2) the lack of consideration of various behavioral drivers (e.g., social, environmental, and economic); (3) simplified data analysis methods, which overlook combined effects of behavioral drivers; and (4) limited scopes of work to either residential or commercial buildings, overlooking potential synergies between the two. This paper aims to fill the stated gaps in the literature by proposing a comprehensive data collection and analysis framework to investigate the energy conservation motivation and actions of people in individual or groups of buildings (e.g., community or city). The framework is illustrated through a case study on a green campus located in Abu Dhabi, United Arab Emirates (UAE). Data was collected from a total of 227 campus users or residents, followed by descriptive and statistical analyses using Principal Components Analysis (PCA) and multiple linear regression models. Results indicate that energy saving awareness and motivation do not directly translate to actions, particularly at the workplace where a correlation coefficient of only 0.083 is observed. Factors such as respondents’ demographics, the level of control over building systems, and motivation drivers (e.g., financial, social, and environmental) highly affect energy saving actions and need through consideration for effective human-focused energy conservation strategies.

Abstract The commercial building sector has become the focus of many governmental energy reduction initiatives to achieve more sustainable development. Reducing building energy use starts by improving the design of buildings. To this end, energy modeling and simulation tools are used during the design phase to predict energy use and help designers choose and size the different building systems. Large discrepancies are however being observed between predicted and actual building energy performances. In order to determine the sources of errors and improve these predictions, the sensitivity of energy models to different input parameters needs to be evaluated. Studies in literature have extensively evaluated the sensitivity of models to the buildings’ technical design parameters. However, none considered the parameters related to the energy consumption behavior of occupants, leaving their impact on energy modeling unknown. This paper presents a comprehensive sensitivity analysis study performed on the occupancy behavioral parameters of typical office buildings of different size and in different weather zones. Significant sensitivity levels were observed, varying according to both building size and weather conditions. The highest sensitivity was obtained when varying the ‘heating temperature set point’ parameter in small-size buildings located in US weather zone 2 Dry.

Abstract Novel electrochromic glazing technology has been identified as an emerging option for reducing cooling and lighting electricity demand. As this technology is particularly promising for office building we assess the related technical energy saving potential in case of nation-wide implementation in Swiss office buildings. A Monte Carlo model of Swiss office building stock using distributions of empirical building characteristics was coupled with a statistical model of energy savings of electrochromic glazing. The building stock model for Swiss office buildings was shown to produce cooling and lighting electricity demand estimates in agreement with the existing case study literature. Total yearly electricity demand for lighting and cooling was calculated to be 1152 ± 32 GWh. Electrochromic glazed saved 125 ± 6 GWh or on average 11% of lighting and cooling electricity demand. Electrochromic glazing was found to be particularly effective in highly cooled office buildings, where cooling accounted for 20 kWh/m2 and total electricity saving potential was estimated at 5.5 kWh/m2 or 5.2% of today’s typical total electricity demand of an office building across all uses. Areas where electrochromic glazing would have particularly high potential are highlighted.

AbstractIn commercial buildings, energy consumed during operation oftentimes differs from that predicted during design. The discrepancy is specifically large in energy-intensive buildings (e.g., laboratory facilities), which can consume up to five times more energy than other types of commercial facilities (e.g., office buildings). Among different factors that impact building performance, recent studies indicate that how occupants use and how facility managers operate the building highly influence energy consumption levels. Consequently, there is a growing need for post-occupancy evaluation (POE) to investigate how human actions impact building performance and identify energy-saving opportunities. Despite advances in the POE field, researchers are still facing important challenges related to collecting, processing, and analyzing relevant building energy and occupancy data. Consequently, current POE methods are not adequate to investigate human-focused energy conservation opportunities in commercial buildi...

Energy consumption estimates obtained during the design phase of commercial buildings typically differ from actual consumption levels measured during the operation phase. One important reason for this variation is that energy estimation software (e.g. eQuest and Energy Plus) consider occupants as static elements with fixed schedules and energy use characteristics, misrepresenting the dynamic aspect of their interactions in the building environment and the resulting changes in their energy consumption patterns. This paper proposes a new approach for energy estimation in buildings using agent-based modeling, a technique capable of simulating occupancy in a dynamic manner. First, occupants are divided into ‘Low’, ‘Medium’, and ‘High’ energy consumers. Then, an agent-based model simulates these occupants’ interactions with each other, with the room environment, and with the exterior. Preliminary results show a difference of more than 20 percent in the design energy consumption estimates for a university building office when using the proposed method.

Abstract In line with global trends towards energy efficiency, Dubai's Government introduced Al Sa'fat Green Building Rating System (GBRS) to reduce the energy intensity of its building sector. This study presents the first comprehensive evaluation of Al Sa'fat with emphasis on building energy performance, shedding light on its effectiveness, limitations, and techno-economic potential. Following a comparison of Al Sa'fat to international and regional GBRSs, a detailed energy-efficiency analysis is conducted using building performance simulations applied to local residential and commercial building archetypes. The benefits of adopting Al Sa'fat at the urban scale are then estimated using a macro techno-economic analysis. Results confirm the potential of Al Sa'fat in curbing current consumption patterns with energy savings ranging from 22% for hotel buildings to 63% for single-family villas. The anticipated monetary savings from adopting Al Sa'fat in Dubai are equally significant and could exceed USD 100 Billion over the next 25 years. Finally, recommendations are made to overcome critical limitations of Al Sa'fat, including the minimal emphasis on water efficiency measures and limited differentiation between its current certification levels (i.e., bronze, silver, gold, and platinum), which could discourage building owners and developers from pursuing high certification levels.

AbstractThis paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.