• Energy Research

Highlights•Common focus points in existing definitions of energy flexible buildings have been identified.•Quantification methods for the prediction of the available energy flexibility of buildings are reviewed.•Comparison of methods on a thermal case study shows significant overlap among indicators.•Time, power and cost are identified as main recurring characteristics.•Optimal control methods are found more appropriate with increasing system complexity.

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.

This study aimed to assess the current status of Fault Detection and Diagnosis (FDD) implementation in building and Heating, Ventilation and Air Conditioning (HVAC) systems in the building industry. Semi-structured qualitative interviews were conducted with 29 experts from different HVAC company types in the building industry. In addition, a literature review was performed to investigate academic research on FDD implementation. The study identified barriers and drivers to implementing FDD systems, these included; technological and technical, economic and business, users, social and societal, and regulatory. An Automatic Fault Detection and Diagnosis (AFDD) implementation matrix was developed to evaluate FDD implementation in building systems, and all interviewed companies were classified based on their FDD knowledge, services, and type. Results show that expert-rule systems are still prevalent in the industry. The literature review revealed a scarcity of FDD implementation studies in academic research due to challenges in testing and validating results in actual building operation conditions. Lastly, this study discusses the key findings: 1) FDD does not sell, 2) Lack of actively engaging and promoting FDD services, 3) FDD seems to be an academic definition, 4) The bottlenecks: The fault handling process and user’s mindset towards FDD, and 5) Governmental regulations and legislatives drive the implementation focus.

For a very long time activities related to efficient domestic hot water (DHW) production and distribution have been neglected and left behind due to an insignificant share in total energy use for buildings. It is in recent years that DHW has emerged as one of the key energy factors in the total energy use in buildings and its share is continuously increasing as energy use in other segments is continuously decreasing, for example space heating, ventilation, and energy for lighting. It becomes suddenly undeniable that efforts in the field of energy-efficient DHW must be strengthened, and as such, there is increased activity in the field. However, the work reported is very dispersed and fragmented. The objective of this review article is to collect and present recent works related to improve performance of a DHW system in terms of energy. The scope and content of the paper aims to address the topics of high relevance to the field, these are shift towards the new situation in which DHW becomes a significant energy use responsible factor in buildings, distribution and weighting of losses related to DHW systems and purpose of DHW use. The article focuses on novel actions to obtain energy-efficient DHW in the following domains: DHW production, DHW distribution and circulation, wastewater heat recovery, and control strategies. The article finishes with conclusions.

One of the initiatives to reach the European decarbonization goal is the roll-out of smart heating meters in the building stock. However, these meters often record the total energy usage with only hourly resolution, without distinguishing between space heating (SH) and domestic hot water (DHW) production. To tackle this limitation, this paper presents the validation of a new methodology to estimate the SH and DHW from total measurements in different building types in three countries (Denmark, Switzerland, and Italy). The method employs a combined smoothing algorithm with a support vector regression (SVR) to estimate the different heating uses. The estimation results are compared with the different countries’ DHW compliance calculations. The comparison showed that the compliance calculations outperformed this method by considering the validation dataset characteristics.

The increasing global energy demand, the foreseen reduction of available fossil fuels and the increasing evidence off global warming during the last decades have generated a high interest in renewable energy sources. However, renewable energy sources, such as wind and solar power, have an intrinsic variability that can seriously affect the stability of the energy system if they account for a high percentage of the total generation. The Energy Flexibility of buildings is commonly suggested as part of the solution to alleviate some of the upcoming challenges in the future demand-respond energy systems (electrical, district heating and gas grids). Buildings can supply flexibility services in different ways, e.g. utilization of thermal mass, adjustability of HVAC system use (e.g. heating/cooling/ventilation), charging of electric vehicles, and shifting of plug-loads. However, there is currently no overview or insight into how much Energy Flexibility different building may be able to offer to the future energy systems in the sense of avoiding excess energy production, increase the stability of the energy networks, minimize congestion problems, enhance the efficiency and cost effectiveness of the future energy networks. Therefore, there is a need for increasing knowledge on and demonstration of the Energy Flexibility buildings can provide to energy networks. At the same time, there is a need for identifying critical aspects and possible solutions to manage this Energy Flexibility, while maintaining the comfort of the occupants and minimizing the use of non-renewable energy. In this context, the IEA (International Energy Agency) EBC (Energy in Buildings and Communities program) Annex 67: “Energy Flexible Buildings” was started in 2015. The article presents the background and the work plan of IEA EBC Annex 67 as well as already obtained results. Annex 67 is a corporation between participants from 16 countries: Austria, Belgium, Canada, China, Denmark, Finland, France, Germany, Ireland, Italy, The Netherlands, Norway, Portugal, Spain, Switzerland and UK.

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.