7 research outputs found

    Data-Driven Key Performance Indicators and Datasets for Building Energy Flexibility: A Review and Perspectives

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    Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 81 data-driven KPIs from 91 recent publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, such as designing utility programs, standardizing energy flexibility quantification and verification processes; and (4) curating datasets with proper description for energy flexibility assessments.Comment: 30 pages, 14 figures, 4 table

    Sustainable and energy-efficient domestic hot water systems:A review

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    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 sys-tems 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

    Validation of a new method to estimate energy use for space heating and hot water production from low-resolution heat meter data

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    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

    Towards automated fault detection and diagnosis in district heating customers: generation and analysis of a labeled dataset with ground truth

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    This study aims to develop a framework for automated fault detection and diagnosis (AFDD) in district heating (DH) substations by comprehensively understanding typical faults. AFDD is presently dependent on manual detection and diagnosis, leading to limitations. To address this issue, the study utilized data from 158 fault reports and smart heat meter data from residential buildings in Denmark to investigate common faults and conduct a fault impact assessment. The study suggests additional indicators for use by DH utility companies to detect anomalies in the future. The findings indicate that greater attention to fault detection and diagnosis can decrease energy usage and return temperatures, demonstrating the significance of AFDD implementation
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