Publications
2023
This article presents a new methodology to disaggregate the energy demand for space heating (SH) and domestic hot water (DHW) production from single hourly smart heat meters installed in Denmark. The new approach is idealized to be easily applied to several building typologies without the necessity of in-depth knowledge regarding the dwellings and their occupants. This paper introduces, tests, and compares several algorithms to separate and estimate the SH and DHW demand. To validate the presented methodology, a dataset of 28 Danish apartments with detailed energy monitoring (separated SH and DHW usage) is used. The comparison shows that the best method to identify energy demand data points corresponding to DHW production events is the so-called “maximum peaks” approach. Furthermore, the best algorithm to estimate the SH and DHW separately is a combination of two methods: the Kalman filter and the Support Vector Regression (SVR). This new methodology outperforms the current Danish compliances typically used to estimate the annual DHW usage in residential buildings.
2022
Well-functioning buildings are crucial for the occupant's health and comfort and for reducing the CO2 emissions from the building sector. A first step in assessing a well-functioning building is to know the current state of the building by, for example, relevant Key Performance Indicators (KPIs). Choosing suitable KPIs to provide a clear message can be challenging; however, beneficial to convey a message to the building actors. This study proposes a Building Assessment Framework to mitigate the latter, consisting of 1) a flexible and novel KPI tool and 2) a step-bystep KPI assessment methodology applicable to all buildings, systems, subsystems, and components. The KPI tool provides the user with a list of KPIs suited for all building systems, and with a separate backend and frontend, it is an easy tool to use. The KPI assessment methodology will guide the user through 5 steps and propose visualization of the chosen KPIs. The step-bystep KPI assessment methodology consists of 5 steps: 1) identification of the selected building resolution level 2) selection of the KPIs for the resolution level 3 + 4) recognition and crossreferencing of necessary sensors 5) choice of benchmarking for the data. The results from the KPI assessment using historical data from a university building located in Denmark demonstrate that the KPI tool is generic, making it applicable to all levels of a building and its systems. The Building Assessment Framework is flexible; it can be used over short and long periods (instantaneous to several years) and implemented in the building management system. However, it is necessary to be used with historical data, allowing for the real-time performance evaluation of the selected buildings or systems, thereby enabling the users to spot potential abnormal behavior that can lead to faults in the systems.
This document describes the development of the SATO KPI Tool in relation to the H2020 SATO Project.
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.
The now widespread use of smart heat meters for buildings connected to district heating networks generates data at an unknown extent and temporal resolution. This data encompasses information that enables new data-driven approaches in the building sector. Real-life data of sufficient size and quality are necessary to facilitate the development of such methods, as subsequent analyses typically require a complete equidistant dataset without missing or erroneous values. Thus, this work presents three years (2018-01-03 till 2020-12-31) of screened, interpolated, and imputed data from 3,021 commercial smart heat meters installed in Danish residential buildings. The screening aimed to detect data from not used meters, resolve issues caused by the data storage process and identify erroneous values. Linear interpolation was used to obtain equidistant data. After the screening, 0.3% of the data were missing, which were imputed using a weighted moving average based on a systematic comparison of nine different imputation methods. The original and processed data are published together with the code for data processing (https://doi.org/10.5281/zenodo.6563114).
Denmark aims to be independent of fossil fuels in the country's energy production by 2050. One of the initiatives to reach the decarbonization goal is the digitalization of the energy sector, specifically the roll-out of smart meters in the buildings connected to the district heating network. As a result, it allowed having better insights into the dynamics of the heating loads of the demand side. However, these meters often record the total energy usage without distinguishing between the energy use for space heating (SH) and domestic hot water (DHW). Additionally, the metered data have hourly resolution, which prevents the detection of short DHW usage. To tackle this limitation and gain valuable information on the buildings' heating patterns, this paper presents a new methodology to estimate the energy use for SH and DHW from total measurements in residential buildings. The method employs a combined smoothing algorithm with a support vector regression to estimate the energy use for SH from outdoor conditions. The energy use for DHW is calculated a posteriori by the difference between the total measurements and the estimated SH energy. The advantage of this technique is the ability to be applied in hourly-resolution data while only requiring local weather measurements, making it a tool to be utilized in different scenarios. This method is validated with three different sets of building cases. The first dataset consists of 28 apartments in Denmark, where the measurement resolution is coarse at 1 kWh. This case focuses on determining the method's accuracy in single-family dwellings when their measurements are truncated. The second dataset set of apartments is located in a 5-story building in Switzerland. In this case, the objective is to test the method's accuracy when analyzing aggregated measurements of all dwellings in the building. The third dataset includes hourly readings from customers connected to a DH network in Italy. In this case, the objective is to test the method's application to other building typologies (i.e., historical buildings). Because these three cases are located in different countries, this validation study also tests the method's robustness to the variability of users, locations, and heating system types.
2020 and older
In Europe, one of the most sustainable solutions to supply heat to buildings is district heating. It has good acceptance in the Northern countries, a low-carbon footprint, and can easily integrate intermittent renewable energy sources when coupled to the electrical grid. Even though district heating is seen as a vital element for a sustainable future, it requires extensive planning and long-term investments. To increase the understanding of the district heating network performance and the demand-side dynamics of the connected buildings, several countries, including Denmark, have installed smart heat meters in different cities. In that context, this paper presents several methodologies to analyze the datasets from the smart heat meters installed in a small Danish town. The first method is concerning data curation to remove the anomalies and missing data points. The second method analyses measured variables (heat consumption, outdoor temperature, wind speed, and global radiation) to acquire new knowledge on the building characteristics. These results were compared with the values given by the energy performance certificates of a smaller sample of 41 households. Finally, to communicate and visualize the analysis outputs in a user-friendly way, an interactive web interface tool has been created.
District heating has been found to be a key component of future and reliable smart energy grids comprising 100% of renewable energy sources for countries with dominant heating season. However, these systems face challenges that require a deeper understanding of the coupling between the distribution networks and the connected buildings, to enable demand-side management and balance the intermittence of renewables. In recent years, many smart energy meters have been installed on the heating systems of Danish dwellings connected to district heating, and the first yearly measurement data sets of large building clusters are now available. This article presents the methodology for the pre-processing and cluster analysis (K-means clustering) of a one-year-long smart energy meter measurement data from 1665 Danish dwellings connected to district heating. The aim is to identify typical household daily profiles of heat energy use, return temperature, and temperature difference between the supply and the return fluid. The study is performed with the free software environment “R”, which enables the rapid extraction of information to be shared with professionals of the building and energy sectors. After presenting the preliminary results of the clustering analysis, the article closes with the future work to be conducted on this study case.
Research projects
01/09/2020 → 31/08/2023
Funding: Horizon 2020 under SOCIETAL CHALLENGES - Secure, clean and efficient energy. Grant agreement ID: 893945 .
See publications and activities in the project.
EDYCE (Energy flexible DYnamic building CErtification) is the natural evolution of the conventional Energy Performance Certification into real-time optimisation of building performance and comfort by capturing the building's dynamic behaviour and at the same time providing transparent feedback through an intuitive interface. E-DYCE will drastically increase the reliability of the energy performance assessment process. It will support communication between labelling professionals and building owners to cultivate benefits in both indoor climate and energy savings. It is complementary to the current EPC labelling method and not competitive, bringing and standing for all the value of the next generation of certification and building energy assessment. In the current generation of steady-state EPCs the free-running potential of buildings is not considered, nor are included in the labelling stock buildings without heating installed or those of old cultural heritage. On the opposite side, buildings' smartness and ability to cope with changes are not rewarded when buildings are labelled. Overheating, low comfort, and demotivation for renovations can be named as results of poor decisions taken through EPCs. The transition to dynamic calculations must be supported by optimised and structured processes: efficient input data acquisition, data storage, interoperability, and transparent presentation and communication with the user.
01/06/2021→31/08/2024
Funding: Independent Research Fund Denmark - Green Transition (Danmarks Frie Forskningsråd (DFF), tematisk forskning -Grøn omstilling)
See publications and activities in the project.
Green Transition calls for lowering energy demand and increased use of intermittent energy sources, such as wind and solar power. Buildings account for 40% of all energy use and, therefore, have a prominent role in this transition. They will need to use less energy and operate in a smart way by meeting the needs of the building owner and the local energy system. Yet, 73% of Danish houses are conventional buildings equipped with energy meters lacking IoT-enabled devices. However, the dynamic heat data will be obligatory collected by smart meters (SM) for every building connected to the district heating grid from 2027. The FOREFRONT project hypothesises that by developing a novel approach to building performance assessment and optimisation based on the real-time data from SM, this new smartness can be beneficial for the building owner in meeting the energy efficiency goals and for the energy sector in increasing the efficient utilisation of buildings' storage capacity for network balancing.
01/12/2020→31/05/2024
Funding: European Union's Horizon 2020 research and innovation programme under Grant Agreement N° 958345.
See publications and activities in the project.
Solutions are needed to holistically address building operation inefficiencies, considering the energy consumption, fossil fuel dependency and CO2 footprint, as well as the wellbeing and economy dimensions. The PRELUDE project is focused on balancing these aspects of building operation, minimising energy consumption based on a free-running strategy, and maximising self-consumption and RES utilisation while maintaining comfortable and healthy conditions. The approach is technology-neutral, scalable from an individual building to the district level and can be applied across any location, typology and smartness level. In PRELUDE, residential buildings will operate dynamically, capable of demand response and flexibility, regardless of pre-existing infrastructure. In line with the need to increase renovation rates and RES penetration in the residential building stock and enabled by powerful assessment and prediction tools, PRELUDE will provide the user (occupant/ tenant, owner/ manager and service provider) with clear and pertinent techno-economic information to make the actions and the right investments at the right time.
Funding: This project receives funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement Number 957128
See publications and activities in the project.
The SATO project will implement a cloud-based platform that can perform self-assessment and optimisation of energy-consuming devices in a building. This platform will use an artificial intelligence approach combined with 3D BIM-based visualisation to provide an accurate vision of the real-life energy performance of buildings and appliances.