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: 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.