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The operational building data presented in this paper has been collected from six office rooms located in an office building (research and educational purposes) located on the main campus of Aalborg University in Denmark. The dataset consists of measurements of occupancy, indoor environmental quality, room-level and system-level heating, ventilation and lighting operation at a 5 min resolution. The indoor environmental quality and building system data were collected from the building management system. The occupancy level in each monitored room is established from the computer vision-based analysis of wall-mounted camera footage of each office. The number of people present in the room is estimated using the YOLOv5s image recognition algorithm. The present dataset can be used for occupancy analysis, indoor environmental quality investigations, machine learning, and model predictive control.

The data presented were sourced from 34,884 commercial smart heat meters and 10,765 commercial smart water meters, spanning a timeframe of up to 5 years (2018–2022). All data primarily originated from single-family houses in Aalborg Municipality, Denmark. Furthermore, comprehensive building characteristics were collected for each building, where available, from the Danish Building and Dwelling Register (BBR) and Energy Performance Certificate (EPC) input data. This effort yielded an extensive pool of up to 86 distinct characteristics per building. All smart meter data were processed employing a well-established methodology, resulting in equidistant hourly data without any erroneous or missing values. The building characteristics derived from the EPCs were additionally filtered using rule sets to improve the data quality. This dataset holds substantial value for researchers involved in the domains of the built environment, district heating, and water sectors.

Measurement(s) Heat energy • Volume flow at supply • Volume flow X return temperature • Volume flow X supply temperature Technology Type(s) Smart heat meter Sample Characteristic - Organism Buildings Sample Characteristic - Location Aalborg

Extensive research demonstrates the importance of user practices in understanding variations in residential heating demand. Whereas previous studies have investigated variations in aggregated data, e.g., yearly heating consumption, the recent deployment of smart heat meters enables the analysis of households’ energy use with a higher temporal resolution. Such analysis might provide knowledge crucial for managing peak demand in district heating systems with decentralized production units and increased shares of intermittent energy sources, such as wind and solar. This study exploits smart meter heating consumption data from a district heating network combined with socio-economic information for 803 Danish households. To perform this study, a multiple regression analysis was employed to understand the correlations between heat consumption and socio-economical characteristics. Furthermore, this study analyzed the various households’ daily profiles to quantify the differences between the groups. During an average day, the higher-income households consume more energy, especially during the evening peak (17:00–20:00). Blue-collar and unemployed households use less during the morning peak (5:00–9:00). Despite minor differences, household groups have similar temporal patterns that follow institutional rhythms, like working hours. We therefore suggest that attempts to control the timing of heating demand do not rely on individual households’ ability to time-shift energy practices, but instead address the embeddedness in stable socio-temporal structures.

This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository.

There is very little knowledge on the occupant actual hot water comfort (temperature and flow), usage practice, and routines (temporal and spatial distribution of hot water usage in a household). This paper describes the results from the total and hot water measurements in two Danish detached houses. The results show that, at the draw-off points, the temperature of 55 °C is never asked by the occupants, not even in the kitchen sink. The domestic water temperature differentiates depending on the function of the draw-off point, with the shower and kitchen taps being most energy- and water-intense. They constitute around 90% of the hot water use in the house. Shower units on average demand for highest temperature (i.e., 35.5 °C to 40.4 °C). Hand washing operates, on average, at temperature between 20.5 °C to 26.5 °C. Average water temperature at the taps located in utility room varies between 23 °C to 26 °C. These in-depth insight in the total and hot water use in two new-built low energy houses, can a) help building professionals designing more efficient hot water installations; b) enhance the research work on energy flexibility buildings by providing knowledge on most energy-intensive draw-off points; and c) facilitate district heating professionals in improving the network performance.