• Energy Research

This paper presents an occupant-centric theory of buildings’ indoor-environmental control systems and their user interfaces. Buildings typically can have multiple devices and systems to maintain indoor-environmental conditions within certain ranges in order to meet occupants’ health and comfort requirements. Therefore, it is important to understand what those ranges are exactly, who defines them, and for whom. Health and comfort sciences offer some broad directions concerning desirable indoor conditions. These are typically formulated in various codes, standards, and guidelines in terms of target values or the set points of control variables. However, preferable conditions may differ at different times and for different individuals. Another question concerns the agency responsible for maintaining the preferred conditions. In some settings, conditions may be centrally controlled via the buildings’ automation systems, whereas in other settings, occupants might have the possibility to control their immediate surroundings. Given these qualifications, the objective of the present inquiry can be stated more precisely. We outline a human-ecologically inspired theory pertaining to the occupants’ perception of and interaction with a building’s indoor-environmental control systems and their user interfaces. Specifically, we explore the operationalization potential of the proposed theory as a compact assessment protocol for the evaluation of buildings’ responsiveness to occupants’ preferences. Initial experiences with the derivative protocol are promising. Nonetheless, in order to be fully applicable in practice, certain challenges must be addressed. These specifically include the need for more robust procedures toward the translation of occupants’ subjective judgments into quantitative evaluation scales.

Abstract Despite the fact that buildings are designed for occupants in principle, evidence suggests buildings are often uncomfortable compared to the requirements of standards; difficult to control by occupants; and, operated inefficiently with regards to occupants’ preferences and presence. Meanwhile, practitioners –architects, engineers, technology companies, building managers and operators, and policymakers – lack the knowledge, tools, and precedent to design and operate buildings optimally considering the complex and diverse nature of occupants. Building on the success of IEA EBC Annex 66 (“Definition and simulation of occupant behavior in buildings”; 2013–2017), a follow-up IEA EBC Annex 79 (“Occupant-centric building design and operation”; 2018–2023) has been developed to address gaps in knowledge, practice, and technology. Annex 79 involves international researchers from diverse disciplines like engineering, architecture, computer science, psychology, and sociology. Annex 79 and this review paper have four main areas of focus: (1) multi-domain environmental exposure, building interfaces, and human behavior; (2) data-driven occupant modeling strategies and digital tools; (3) occupant-centric building design; and (4) occupant-centric building operation. The objective of this paper is to succinctly report on the leading research of the above topics and articulate the most pressing research needs – planned to be addressed by Annex 79 and beyond.

AbstractWithin a study, an open plan area and one closed office in a university building with a floor area of around 200 m2 were monitored. The present data set covers a period of one year (from 2013-01-01 to 2013-12-31). The collected data pertains to indoor environmental conditions (temperature, humidity) as well as plug loads and external factors (temperature, humidity, wind speed, and global irradiance) along with occupants’ presence and operation of windows and lights. The monitored data can be used for multiple purposes, including the development and validation of occupancy-related models.

In recent years, the building regulations regarding buildings’ thermal performance have been steadily tightened. Such regulations target primarily new buildings. However, construction of new and more energy-efficient buildings alone cannot bring about a significant reduction of the building sector’s negative environmental impact. This implies the importance of thermally retrofitting the existing building stock. Thermal retrofit of buildings typically addresses building envelope as well as building systems for heating, cooling, and ventilation. Both normative calculations and dynamic simulation can be used to estimate the impact of retrofit measures on buildings’ performance. However, past studies have revealed discrepancies between the computationally projected and the actually monitored post-retrofit energy performance of buildings. Such discrepancies, commonly referred to as energy performance gap, must be better understood, if the potential of thermal retrofit measures toward energy conservation and reduction of greenhouse gas emissions is to be more reliably realized. In this context, this paper compares the energy demand of a number of apartments in a multi-unit residential building in Vienna, Austria before and after thermal retrofit. In the course of the study multiple sources of information were considered, including original building plans, details of the retrofit measures, calculated post-retrofit energy demand, and energy bills over different years. Based on this data, the actual pre-retrofit energy performance of the selected units could be compared with the respective post-retrofit values. Moreover, the units’ actual performance could be compared with computed values (obtained via energy certificates and dynamic simulation). The gap between expected and monitored energy performance was analyzed and potential contributors to this gap were discussed.

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.

The term “performance prediction” commonly denotes the analysis of building designs, for instance, in view of their future energy demand. The notion of “performance gap” is invoked, when actual performance of buildings does not match preceding predictions. There has been a recent trend to attribute the so-called energy performance gap predominantly (sometimes even entirely) to user behavior. As a consequence, a number of research efforts (subsumed hear as “the performance gap research program”) pursue more accurate predictions of user behavior and the exploitation of user-related energy efficiency potential via behavioral modification. In the present contribution, we critically revisit the premises and orientation of these efforts. Firstly, we suggest to situate the related discourse within a structured conceptual framework to objectively discuss the spectrum of potential contributors to the errors in building energy use predictions. Secondly, we examine the frequently purported utility of probabilistic methods and uncertainty analysis as an effectual remedy to the problem of performance gap.