Publications

Authors: Giacomo Chiesa, Andrea Avignone, Tommaso Carluccio
University: Department of Architecture and Design, Politecnico di Torino, Italy

Smart building issues are critical for current energy and comfort managing aspects in built environments. Nevertheless, the diffusion of smart monitoring solutions via user-friendly graphical interfaces is still an ongoing issue subject to the need to diffuse a smart building culture and a low-cost series of solutions. This paper proposes a new low-cost IoT sensor network, exploiting Raspberry Pi and Arduino platforms, for collecting real-time data and evaluating specific thermal comfort indicators (PMV and PPD). The overall architecture was accordingly designed, including the hardware setup, the back-end and the Android user interface. Eventually, three distinct prototyping platforms were deployed for initial testing of the general system, and we analysed the obtained results for different building typologies and seasonal periods, based on collected data and users’ preferences. This work is part of a large educational and citizen science activity.

Authors: Giacomo Chiesa, Francesca Fasano, Paolo Grasso
University: Department of Architecture and Design, Politecnico di Torino, Italy

Energy consumption for space cooling is characterized by a continuously rising trend. In parallel, the number of installed domestic cooling units is significantly growing, as confirmed by International Energy Agency (IEA) documents and by yearly reports of sector-specific companies. The global penetration of the air conditioning market is, in fact, quickly increasing, especially in Asia, but also in the Americas and in Europe. This trend is connected to several causes, including climate change, urban heat islands, comfort culture, and building design choices that are inconsistent with respect to local climate. It is hence evident that alternative solutions for cooling, when environmental conditions are favourable, may be adopted and developed in order to reduce cooling energy needs and consequent GHGs (greenhouse gas emissions). Among low-energy alternatives, ventilative cooling (VC) is a valuable technique to reduce energy needs and consumption in buildings supporting free-running and/or fan-assisted ventilation for space cooling. This technique was demonstrated to be very effective in reducing overheating risks, but also to guarantee thermal and IAQ (indoor air quality) comfort in buildings during the summer season. However, the ventilative cooling potential occurs when external air temperatures are below comfort thresholds; therefore, its applicability is local and time-specific and is connected to local climate/weather conditions. As underlined for the majority of passive and low-energy cooling solutions, the non-homogeneous specific local potential has limited the current applications of VC with respect to passive heating technologies, that is sunspaces. Nevertheless, a consistent need to support the diffusion of VC solutions is evident, given this approach is not sufficiently covered and valorised by current regulations – see, for example, the recent analysis reported in – even in those climates in which it may support thermal comfort without cooling system activations during the majority of hours. This requires the development of methodologies to calculate the potential of ventilative cooling – see, for example, the results of the IEA EBC ANNEX 62 on Ventilative Cooling and other references supporting the widespread of VC.

Authors: Daniel Leiria, Hicham Johra, Anna Marszal-Pomianowska, Michal Zbigniew Pomianowski
University: Department of the Built Environment, Aalborg University, Denmark

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.

Authors: Juha Koskela, Pertti Järventausta
University: Unit of Electrical Engineering, Tampere University, Finland

Distributed electric power production by small-scale customers is increasing continuously. Photovoltaic production is a popular method of producing self-energy for customers. Additionally, power systems require more flexibility when weather-dependent renewable energy production increases. Small-scale customers can increase the self-consumption of self-produced energy by using batteries or a demand response operation. However, batteries require high investment, and demand response operations induce a loss of comfort. Customers who heat their buildings using electric heaters are a good target for demand response operations because their heating can be controlled with limited changes in the indoor temperature. The demand response potential of a building can be defined by simply using customer load profiles and knowledge of the outdoor temperature. Any other information is not required in the proposed novel method. A tolerable variation in indoor temperature corresponds to considerably smaller battery capacity, though it is still a significant amount. With an optimally sized photovoltaic system, it is possible to use both methods simultaneously to increase self-consumption. Maximal benefits can be attained from both methods if the battery system is used as a primary control and the demand response is used as a secondary control. The defined novel method for determining the demand response potential of small-scale customers can also be used when estimating the flexibility of a large customer group. Small-scale customers together can provide significant flexible capacity when their electrical heating is centrally controlled.

Authors: Peter Klanatsky, François Veynandt, Christian Heschl
University: Burgenland University of Applied Sciences, Pinkafeld, Austria

Model predictive control (MPC) can improve energy efficiency and demand-side flexibility in buildings. Developing a grey-box model suitable for MPC is not straightforward, especially in buildings combining, not only ventilation and usual internal loads, but also Thermally Activated Building Structures (TABS) and large glass façades with external shading. To address these complexities, this paper presents a reduced order grey-box approach, considering all these elements. Various single zone model structures are compared, combining resistance–capacitance model, with finite difference or finite volume methods for modelling the TABS. The performance of these various model structures is evaluated using experimental data from a well-equipped living laboratory building. Additionally, the influence of technical parameters on the model’s performance is investigated.
The best model variant, with an enhanced glass façade model, achieves an accuracy of 0.25 ◦C of Mean Absolute Error over a year of simulation, on the 24 h zone temperature forecast compared to the measurement. This model has a small number of parameters (8), which are estimated with the least square non-linear method. The stability of the parameter values is analysed. The parameter identification requires only a small historical dataset of 1–2 weeks for startup and 2–4 weeks for training. This provides an adaptive model, in the sense that it is updated regularly (every day or week) based on recent measurement data. This data-driven evolving model is suitable across a wide range of applications involving data-driven Model Predictive Control (MPC) for buildings.

Authors: P. Klanatsky, F. Veynandt, R. Stelzer, C. Heschl
University: University of Applied Sciences Burgenland, Campus 1, A-7000 Eisenstadt, Austria

The dataset provides all necessary variables for data-driven energy modelling of an office room. The measurement data have been obtained from an office building operating as living lab in a temperate climate of Central Europe. The temperatures and the ventilation air flowrate are raw mea- surements, while the heat flows are calculated from mea- surements. The incoming solar irradiance is calculated with two façade models –simple and enhanced–, using measure- ments (solar irradiance, movable shading settings) and build- ing characteristics (geometry, glazing and shading proper- ties). One year and four months of data is provided with a fine one-minute time step and a coarser fifteen-minute time step. The dataset can be used to test and validate data-driven models, for example for predictive control applications.

Authors: Kais Dai, Esteban Fabello González, Rebeca Isabel García Betances
Tree Technology S.A, Calle San Francisco, 2 – 4th floor, Oviedo, 33003, Spain

This paper presents a privacy-preserving Multi-Center Federated Learning (MCFL) framework for district heating demand forecasting with a 24-hour prediction horizon. To evaluate the effectiveness of this framework, we conducted a comparative analysis across three models: a monolithic model, a traditional federated learning (FL) model, and the proposed MCFL model. Our results demonstrate that the MCFL model improves the prediction accuracy of the standard FL model by 13.86%, suggesting it as a promising enhancement in federated settings. Furthermore, MCFL is particularly well-suited for district heating forecasting, as it handles data heterogeneity, reinforces privacy protections, and supports scalability, making it an ideal choice for complex, distributed environments.

Authors: Peter Klanatsky, François Veynandt, Christian Heschl
University: Burgenland University of Applied Sciences, Campus Pinkafeld, Steinamangerstraße 21, 7423 Pinkafeld, AUSTRIA

This study presents the development and testing of a Data-driven Model Predictive Control (DMPC) strategy for optimizing energy efficiency in buildings with glass façades, shading systems, and Thermally Activated Building Structures (TABS). The DMPC approach utilizes a grey-box state space model with data-driven parameter identification. The optimization algorithm is Mixed-Integer Linear Programming (MILP). It allows accounting for thermal comfort through constraints, while the objective function minimizes energy or costs in a reduced solution space. A detailed simulation environment has been developed for the investigations and validated through long-term experiments on a real building. Based on full-year simulations, the DMPC was compared against standard rule-based controllers, varying parameters such as shading control, prediction horizon, and optimization frequency. Results show that the DMPC consistently outperforms traditional control methods, achieving lower energy consumption and costs while maintaining comfortable room temperatures. Across the simulated variants, the DMPC reduced total energy demand –and similarly the associated costs– for the investigated zones in a range of 37% to 47%, compared to the best variant of the rule-based controller with automatic shading control. The robustness of the DMPC algorithm across various settings demonstrates its potential for practical implementation in building energy management systems, particularly in structures with significant solar gains and thermal mass.

Authors: Peter KLANATSKY, François VEYNANDT, Christian HESCHL, Roman STELZER, Georgios SIOKAS, Athanasios BALOMENOS, Panagiotis ZOGAS

Data-driven Model Predictive Control (DMPC) is emerging as a promising approach for advanced building control systems, enhancing energy efficiency and leveraging demand-side flexibility while maintaining occupant comfort. Given the increasing diversity and complexity of modern buildings, optimal control and energy management are crucial enablers for achieving the United Nations’ Sustainable Development Goals. To address these challenges, this study implements and evaluates a newly developed centralized adaptive model-based DMPC system in an office building located in a temperate climate zone over a full year. The system utilizes reduced-order state-space (resistance-capacitance) models for each building zone and simplified representations of storage tanks and heat pumps for the Heating, Ventilation, and Air Conditioning (HVAC) equipment. Professional weather forecasts with a 24-hour prediction horizon are integrated into the control strategy. Significant energy savings were achieved, by combining the DMPC with complementary Energy Conservation Measures (ECMs), primarily transitioning from a gas boiler and air source heat pump, to a ground source heat pump. Compared to the baseline scenario, adjusted according to the Measurement and Verification (M&V) framework, the implemented system demonstrated a 50% reduction in overall heating and cooling energy consumption (34% for heating and 79% for cooling), while the final energy use for heating and cooling was divided by a factor of more than 6 (-85%). Additionally, the DMPC system effectively harnessed the building’s demand-side flexibility potential, leading to improved operating costs and extended system lifetime. These results highlight the efficacy of DMPC in real-world applications and its potential to contribute significantly to building energy efficiency and sustainability goals, demonstrating its value in achieving both environmental targets and operational improvements in the building sector.

Authors: Daniel Leiria, Hicham Johra, Justus Anoruo, Imants Praulins, Marco Savino Piscitelli, Alfonso Capozzoli, Anna Marszal-Pomianowska, Michal Zbigniew Pomianowski
University: Aalborg University, Department of the Built Environment, Aalborg, Denmark – Politecnico di Torino, Department of Energy, TEBE Research Group, BAEDA Lab, Turin, Italy – SINTEF Community, Department of Architectural Engineering, Oslo, Norway

This study explores the challenges and advancements in collecting ground-truth data to enhance fault diagnosis models for district heating systems. Initiated by the need to address limitations in previous data collections, this research leverages an enriched dataset from a Danish district heating utility to identify faults in household substations. Despite some inaccurate fault categorizations, complex fault patterns, and truncated measurements, the analysis of 50 detailed cases out of 127 fault reports reveals that, while return temperature reliably indicates faults, energy usage patterns do not. By employing self-organizing maps combined with k-means clustering, fault symptoms and patterns were categorized adequately, demonstrating the utility of high-dimensional data clustering in fault diagnosis. Additionally, an algorithm using time series decomposition is suggested to identify extreme and subtle anomalies, enhancing fault detection capabilities. The paper concludes that these methodologies significantly improve the accuracy and dependability of fault diagnostics in district heating systems, paving the way for more efficient operational management.

Authors: May Zune, Maria Kolokotroni
University: College of Engineering, Design and Physical Sciences, Brunel University London, UK

Occupants in residential buildings usually control natural ventilation through window openings. However, few studies have developed simple rules based on the outdoor weather forecast that can inform the occupants to predict the indoor condition by applying natural ventilation for thermal comfort and indoor air quality (IAQ). This paper describes a model based on indoor/outdoor correlations, derived through simulations using EnergyPlus and CONTAM, to help occupants maintain internal environmental quality manually or through simple controls. Simulation test cases were defined considering factors that can statistically change correlations, including the effect of single-sided and cross-ventilation, trickle ventilators, different schedules for window opening, heating and occupancy, size of the model, and building orientation for the window opening. The study found strong correlations between external and internal hourly temperatures, as well as between airflow and wind speed, and the inverse temperature differences between outdoor and indoors. The derived model consists of coefficients of determination (R2) between the correlated parameters and a set of equations to calculate thermal comfort and pollutant concentrations in the space. The derived correlations are then used independently to predict internal operative temperature and ventilation rates. Based on these parameters, thermal comfort is evaluated for the next period (hours or days) to predict overheating (based on the adaptive thermal comfort model) and indoor concentrations using contaminant mass balance equations for indoor CO2 concentration. An example of the application of this model is presented for a location in central Europe where a pilot building of the PRELUDE H2020 project is located. The findings of this study indicate how to reduce a large amount of data down to a manageable form, useful for occupants to identify indoor conditions for their space based on climatic conditions. This study highlights the importance of a user-driven decision-making process for predicting the indoor conditions from outdoor climatic parameters which could encourage behavioural change strategies and effective use of natural ventilation for thermal comfort and IAQ.

Authors: Daniel Leiria, Hicham Johra, Evangelos Belias2, Davide Quaggiotto, Angelo Zarrella, Anna Marszal-Pomianowska, Michal Pomianowski
University: Department of the Built Environment, Aalborg University, Denmark

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.

Authors: May Zune, Maria Kolokotroni
University: College of Engineering, Design and Physical Sciences, Brunel University London, UK

This study presents the development of a climate correlation model encompassing the impacts of diverse climatic parameters for the indoor conditions prediction concerning thermal comfort and indoor air quality (IAQ). We investigated the relationship between outdoor and indoor conditions in free-standing small houses, and compared the results of two contrasting European climates – Nordic and Mediterranean. The impacts of ventilation modes on the IAQ – infiltration and natural ventilation through window openings – were compared using a black-box model generated in the CONTAM and EnergyPlus simulation engines. The effects of ventilation and heating schedules, model size, and orientation for prevailing wind were tested considering factors that could statistically change correlation equations. The correlations between dry bulb temperature, operative temperature, temperature differences between indoor and outdoor, and airflow were analysed to identify significant patterns or trends between variables without controlling or manipulating any of them. The results were evaluated using adaptive thermal comfort equations and equations to estimate space-specific indoor CO2 concentrations. The study informed the importance of user-driven decision-making processes for predicting the indoor conditions from outdoor climatic parameters which could encourage behavioural change for building operation to improve building thermal comfort and IAQ through natural ventilation strategies.

Authors: Leiria D., Schaffer M., Johra H., Marzsal-Pomianowski A., Pomianowski M. Z.
University: Aalborg University, Department of the Built Environment, Aalborg, Denmark

The EU aims to digitize the building stock across all member states to better understand energy use and achieve energy efficiency goals to address climate change. Smart heat meters are currently used for billing purposes in district heating (DH) grids. Their data is recorded as integer kWh values, which restricts usability for the modeling and analysis of DH networks. Previous research devised a methodology to estimate space heating (SH) and domestic hot water (DHW) energy from total heating data, but the data truncation process reduced accuracy. This study integrates the SPMS (Smooth–Pointwise Move–Scale) algorithm, which estimates decimal values from DH truncated measurements, to improve the accuracy of the DHW and SH disaggregation methods. The study applies these two methodologies to a dataset of 28 Danish apartments and compares the results against full-resolution and truncated data to evaluate performance. Another dataset, named “optimal dataset” is also assessed to determine overall estimation accuracy. Results show that SPMS reduces the disaggregation methodology error of SH and DHW compared to the truncated data. The optimal dataset outperforms the current methodology, indicating a potential for improving and scaling the methodology for larger datasets.

Authors: Leiria D., Andersen K. H., Melgaard S. P., Johra H., Marszal-Pomianowska A., Piscitelli M. S., Capozzoli A., Pomianowski M. Z.
University: Aalborg University, Department of the Built Environment, Aalborg, Denmark – Politecnico di Torino, Department of Energy, TEBE Research Group, BAEDA Lab, Turin, Italy

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.

Authors: Schembri J., Stojceska V. and Kolokotroni M.
7th Global Energy Meet (GEM) Conference, March 06-10, 2023, Boston, MA

Authors: Kolokotroni M. and May Z.
11th International Conference on Sustainable Development in the Building and Environment Conference, Helsinki, Finland 14-18 August 2023

Authors: Kolokotroni M., May Z., Tun T. P., Christantoni I., and Tsakanika D.
43nd AIVC conference: Ventilation, IEQ and health in sustainable buildings, 4-5 October 2023, Conference, Copenhagen, Denmark

Authors: B. Paule, F. Flourentzou, T. de Kerchove d’Exaerde, J. Boutillier, N. Ferrari
Estia SA, EPFL Innovation Park, 1015 Lausanne, Switzerland

In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a “Building Renovation Roadmap”(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of inference rules that will make it possible. On the basis of a diagnosis such as the Energy Performance Certificate, it will help establishing a list of priority actions. The dynamics that drive this project permit to decrease the subjectivity of a human decisions making scheme. While simulation generates digital technical data, interpretation requires the translation of this data into natural language. The purpose is to automate the translation of the results to provide advice and facilitate decision-making. In medicine, the diagnostic phase is a process by which a disease is identified by its symptoms. Similarly, the idea of the process is to target the faulty elements potentially responsible for poor performance and to propose remedial solutions. The system is based on the development of fuzzy logic rules [2],[3]. This choice was made to be able to manipulate notions of membership with truth levels between 0 and 1, and to deliver messages in a linguistic form, understandable by non-specialist users. For example, if performance is low and parameter x is unfavourable, the algorithm can gives an incentive to improve the parameter such as: “you COULD, SHOULD or MUST change parameter x”. Regarding energy performance analysis, the following domains are addressed: heating, domestic hot water, cooling, lighting. Regarding the parameters, the analysis covers the following topics: Characteristics of the building envelope. and of the technical installations (heat production-distribution, ventilation system, electric lighting, etc.). This paper describes the methodology used, lists the fields studied and outlines the expected outcomes of the project.

Authors: N. Dumas, F. Flourentzou, J. Boutillier, B. Paule, T. de Kerchove d’Exaerde
NSB 2023: 13th Nordic Symposium on Building Physics, 12-14 June 2023, Aalborg, Denmark

Authors: Chiesa G., Gasso P., Fasano F.
CISBAT 2023, 13-15 September 2023, conference, Lausanne, Switzerland

Authors: Lummi K., Koskela J., Järventausta P.
EEM2023 conference, Lappeenranta, June 2023

Authors: Koskela J., Lummi K., Järventausta P.
EEM2023 conference, Lappeenranta, June 2023

Authors: Lummi K., Koskela J., Järventausta P.
CIRED 2023 conference, Rome, June 2023

Authors: Valta, J., Lummi, K., Vanhanen, T., & Järventausta, P.
European Energy Markets 2023

Authors: Valta, J., Vernay, A.-L., Vanhanen, T., Castaño-Rosa, R., & Saari, U.
In 2023 Innovative Smart Grid Technologies-Asia (ISGT-Asia) (pp. 1-6). IEEE.

Authors: Huosianmaa, T., Valta, J., & Saari, U.
In 2023 Innovative Smart Grid Technologies-Asia (ISGT-Asia) (pp. 1-6). IEEE.

Authors: Lummi K., Koskela J., Järventausta P.
12th IEEE PES Innovative Smart Grid Technologies Conference, Asia, November 2023

Authors: M. Schaffer, D. Leiria, J. E. Vera-Valdés, A. Marszal-Pomianowska
University: Department of the Built Environment and Department of Mathematical Sciences, Aalborg University, Aalborg, Denmark

Recent research has demonstrated the fundamental potential of smart heat meter (SHM) data. However, it has also been shown that the usability of the data is reduced because SHM energy measurements are commonly rounded down (truncated) to kilowatt-hour values. This study therefore investigates, for the first time, the error introduced by truncation using a high-resolution dataset. Furthermore, a method is developed to reduce the loss of information in the truncated data by combining smoothing with a ruleset and scaling approach (SMPS). SMPS is shown to increase the pointwise accuracy and correlation of the truncated data with the full-resolution data.

Authors: Georgios Siokas, Athanasios Balomenos, Evangelos Fekas, Georgios Triantafyllis, Yannis Kopsinis

Energy conservation measures (ECMs), also referred to as interventions, are a set of actions intended to enhance energy efficiency or conserve energy consumption. When implementing an intervention in a building or a set of buildings, the actual energy savings cannot be directly quantified. Alternately, savings are calculated by comparing the energy consumption before and after interventions while adjusting to condition changes. Based on the literature, systematic approaches for determining the true impact of an investment in energy efficiency are collectively referred to as measurement and verification (M&V) methods, supported by a well-established set of guidelines, directives, and protocols, e.g. International Performance Measurement and Verification Protocol (IPMVP).

Therefore, the paper proposes a methodology based on the available M&V methods to validate, quantify, monitor, and report the achieved energy savings with large-scale aggregated during an operational year. It is designed to integrate and consolidate the available information and provide insights to validate the impact of the energy consumption optimisation strategy. The method describes all the steps, from gathering the data to feature selection to model selection and saving quantification and performance evaluation.

The proposed methodology’s practical application is demonstrated through its testing on Danish residential buildings in the Municipality of Aalborg. A shared adjusted baseline model is constructed per cluster of buildings to predict post-intervention energy consumption. The results show that the method reduces the computational effort of calculating the energy consumption of a series of buildings separately and delivers reliable predictions, underscoring its practical value.

Authors: Aristodemou E., Stojceska V., Christantoni I., Tsakanika D. and Kolokotroni M.
University: College of Engineering, Design and Physical Sciences, Brunel University London, UK

This study evaluates the environmental and economic performance of the “ESTIA of Athens” residential building considering its energy consumption and greenhouse gas (GHG) emissions. It also explores the potential for improvements in these areas. The European Union’s strategy to reduce GHG emissions from buildings by 55% compared to 1990 levels by 2030, as part of its broader aim for climate neutrality by 2050 is a background for the study. The methodology involves a Life Cycle Assessment (LCA) using SimaPro software, monitoring energy consumption, temperature and humidity of the building. The thirteen impact categories and damage impact assessment that include human health, ecosystem and resources were examined. The study compares various scenarios, including historical data from 2018 to 2022 and two hypothetical scenarios to assess environmental impacts across different categories. Additionally, Life Cycle Costings (LCC) are performed to evaluate the economic aspects of the building’s performance. The results highlight substantial differences in energy consumption, GHG emissions and economic costs among the scenarios. The findings suggest that a hypothetical scenario, referred to as Case 3, demonstrates lower environmental impacts and economic costs compared to other scenarios, indicating its potential as an optimal renovation strategy for the building. This includes reductions of 32%, 40% and 58% in the human health, ecosystems, and resources categories, respectively. A similar trend is observed across the impact categories, with reductions ranging from 4% in the Mineral Resource category to 47% in the Global Warming category. The study underscores the importance of holistic assessments in informing energy policy and renovation strategies for achieving both environmental sustainability and economic viability in buildings.

Authors: Anna Marszal-Pomianowska, Daniel Leiria, Hicham Johra, Michal Pomianowski, Imants Praulins, Justus Chigozie Abiodun Anoruo
University: Aalborg University, Thomas Manns Vej 23, 9220 Aalborg, Denmark

Heating, cooling and production of domestic hot water are the dominating energy uses in residential buildings. District heating (DH) provides the thermal energy for these uses to 65% of the Danish homes. However, 50–60% of the DH substations in these dwellings operate with an error, often leading to inefficient operations (i.e., high temperature or high-volume flow of the return heat-carrier fluid in the DH network). This hinders decarbonising the heating sector.
The roll-out of smart heat meters allowed access to the hourly heat demand profiles of the DH customers. In turn, this unlocks the creation of data-driven methods for identifying faulty household systems. However, this cannot be done correctly without the knowledge of the fault types occurring in domestic installations. The present study analyzes 382 reports made by the DH technicians during onsite visits to the houses identified as “faulty” customers. The collected onsite information shows that more than 30% of faults stem from wrong control settings in the space heating or domestic hot water installations. These faults can be fixed with almost no additional cost to the building owner and with the immediate decrease of the return temperatures to the DH system.

Authors: François Veynandt, Bernhard Derler, Christian Heschl
University: University of Applied Sciences Burgenland, Steinamangerstraße 21, A-7423 Pinkafeld,  Austria

In the realm of building performance optimization, understanding occupancy dynamics is pivotal for enhancing both energy efficiency and occupant comfort. Occupancy forecasts, serving as critical inputs for data-driven predictive control technologies, play a significant role in this domain. To address this need, we propose a novel model that directly estimates building occupancy levels. This model is particularly applicable to buildings equipped with mechanical ventilation systems and CO2 concentration sensors. The number of persons is estimated by utilizing the CO2 production rate of people and applying the principle of mass
conservation. The CO2-based approach has been validated with manually recorded ground-truth measurements. A forecast is generated using the first order Markov chain model in combination with an Agent-Based Modell (ABM). The probability transition matrix of the Markov chain defines the behaviour of the occupant-agents, which is used in the ABM to generate behaviour profiles. The model has been tested on four office rooms, with a one-year measurement dataset. The Markov chain with ABM provides a forecast, which encompasses the stochasticity of people’s behaviour. The presence True Positive Rate (TPR) reaches 50 % and the False Positive Rate (FPR) is 15 %, in average. The occupancy TPR is only 30 % and the FPR 15 %. The proposed approach offers a framework to easily implement further variables, like occupancyrelated power consumption, lighting operation, window opening etc.

Authors: Michal Pomianowski, Martin Frandsen, Simon Pommerencke Melgaard
University: Aalborg University, Thomas Manns Vej 23, 9220 Aalborg, Denmark

Radiant heating is a well-established solution that is widely adopted in existing and newnearly zero energy buildings (NZEBs). Underfloor heating (UFH) is known for its many advantages, e.g., allowing access to low-grade heat sources such as heat pumps and low temperature district heating (LTDH) systems, and secure comfortable indoor environment. This study presents a long-term monitoring campaign of an NZEB building fully covered with a UFH system. Monitoring covers approximately one entire heating season, with the experiment aiming to decrease the operational indoor temperature and energy use for space heatingwhile investigating the occupants’ acceptance to this new thermal comfort situation and registering their feedback about perceived thermal comfort. The experiment results show that there is a significant reduction in the operation of the hydraulic system and water circulation in the UFH that is negatively perceived by the occupants who are used to feeling the warm floor. The paper highlights that the UFH, in combination with occupant’s comfort preferences, can be the reason for NZEBs to operate with elevated indoor temperatures and to use more energy than anticipated. Results indicate that UFH in bathrooms is operated all year long due to occupants striving to feel the warm floor. The findings can significantly contribute to the advancement of innovative control strategies for achieving thermal comfort in NZEB buildings with radiant water-based heating systems and consideration for other placement of radiant emitters in the spaces.

Authors: Veit, Martin; Sørensen, Christian Grau; Pomianowski, Michal Zbigniew; Johra, Hicham; Marszal-Pomianowska, Anna
University: Aalborg University, Department of the Built Environment, Thomas Manns Vej 23, DK-9220 Aalborg Ø, Denmark

A step-by-step description of how to set up a database using SQLite and Python is given in this report, along with how to download the necessary software. The report is written such that people without programming knowledge can set up and retrieve data from an SQL database using Python. SQL stands for Structured Query Language. It can be used to store, retrieve and manipulate data efficiently, such that data is not stored in local files on different computers, but can instead be stored on a common platform. This also allows users to not retrieve all the data and load it into memory, but can instead retrieve only the data that is necessary, to alleviate the data handling stage.