No abstract

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.

The research areas of occupant sensing and occupant behavior modeling are lacking comprehensive public datasets for providing baseline results and fostering data-driven approaches. This data descriptor covers a dataset collected via sensors on room-level occupant counts together with related data on indoor environmental quality. The dataset comprises 44 full days, collated in the period March 2018 to April 2019, and was collected in a public building in Northern Europe. Sensor readings cover three rooms, including one lecture room and two study zones. The data release contains two versions of the dataset, one which has the raw readings and one which has been upsampled to a one-minute resolution. The dataset can be used for developing and evaluating data-driven applications, occupant sensing, and building analytics. This dataset can be an impetus for the researchers and designers to conduct experiments and pilot studies, hence used for benchmarking.