Buildings’ expected (projected, simulated) energy use frequently does not match actual observations. This is commonly referred to as the energy performance gap. As such, many factors can contribute to the disagreement between expectations and observations. These include, for instance, uncertainty about buildings’ geometry, construction, systems, and weather conditions. However, the role of occupants in the energy performance gap has recently attracted much attention. It has even been suggested that occupants are the main cause of the energy performance gap. This, in turn, has led to suggestions that better models of occupant behavior can reduce the energy performance gap. The present effort aims at the review and evaluation of the evidence for such claims. To this end, a systematic literature search was conducted and relevant publications were identified and reviewed in detail. The review entailed the categorization of the studies according to the scope and strength of the evidence for occupants’ role in the energy performance gap. Moreover, deployed calculation and monitoring methods, normalization procedures, and reported causes and magnitudes of the energy performance gap were documented and evaluated. The results suggest that the role of occupants as significant or exclusive contributors to the energy performance gap is not sufficiently substantiated by evidence.

Building data monitoring can provide performance feedback for operational optimization of existing facilities and improve future designs. It can support, amongst other things, energy and performance contracting, smart load balancing, and model-predictive building systems control. However, a closer look at the current practice suggests that the commonly deployed technical infrastructures are not mature enough and their hardware resilience and software interoperability are in need of improvement. To address these issues at a fundamental level, we introduce an ontology for the representation and incorporation of multiple layers of monitored building data in pertinent computational applications. The proposed ontology is grounded on the identification of six basic data categories, namely inhabitants, indoor environmental conditions, external environmental conditions, control systems and devices, equipment, and energy flows. The paper illustrates in detail how these data categories provide an effective classification framework to accommodate the multiplicity of empirical information obtainable from building monitoring systems.

The control actions by the inhabitants of buildings can significantly affect the energy performance of buildings and their environmental impact. However, relatively few studies have investigated such actions in detail and on a long-term basis. In most buildings, occupants operate control devices such as windows, shades, luminaries, radiators, and fans to bring about desirable indoor environmental conditions. Knowledge of such user actions is crucial for accurate prediction of building performance (energy use, indoor climate) and effective operation of building service systems. The present contribution describes an effort to observe control-oriented occupant behavior in office buildings in Austria over a period of 1 year. Thereby, user control actions as related to one or more of the building systems for ambient lighting, shading, window ventilation, and heating were monitored together with indoor and outdoor environmental parameters. The collected data has been analyzed to explore relationships between the kinds and frequency of the control actions and the magnitude and dynamism of indoor and outdoor environmental changes. Moreover, implications of user actions for energy use are studied.

Building performance assessment applications require multiple categories of input information. These include, aside from building construction and systems and external conditions, representations of inhabitants. It has been suggested that the representation of people as passive and static entities is unlikely to yield reliable building performance assessment and building operation planning. Rather, adequate representations of building inhabitants should account for dynamics of inhabitants’ presence in buildings and their control-oriented actions (e.g. interactions with buildings indoor environmental control devices and systems). To address these requirements, many recent model development efforts have explored the potential of advanced mathematical formalisms. However, the resulting occupancy-related behavioural models have rarely gone through a rigorous evaluation process. The present contribution is indeed motivated primarily by the lack of explicit procedures and guidelines for the evaluation of proposed user-related behavioural models. Specifically, we formulate a number of conditions that are necessary for systematic and dependable quality assessment of buildings’ inhabitants. Towards this end, we discuss both general model evaluation requirements and specific circumstances pertaining to behavioural models of building inhabitants. By using specific instances of such models, we intend to identify the requirements of a rigorous quality assurance process with regard to behavioural models in building performance assessment applications.