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.