Author: Sascha Hammes

Decision support tools play a key role in helping players such as architects, specialist planners, One-Stop-Shops (OSS) and building owners in the refurbishment process to identify and propose the most suitable concepts from the current variety of systems, depending on the given requirements, e.g., building typology, climate and degree of utilization.

The utilization phase has the greatest energy demand over the life cycle of a building, resulting in particular from heating, cooling, ventilation and electrical consumers. Heat losses via the building envelope are among the most important consumers [Gilani 2021]. Improving the thermal building envelope in particular therefore proves to be an essential measure for reducing energy demand in the building sector. The greatest potential for improvement at EU level lies in single-family houses, followed by multi-family houses [Nemry 2010]. In the context of the building envelope, Nemry et al. identify additional roof insulation, additional façade insulation and better sealing to reduce ventilation as key measures for increasing energy efficiency.

Against the backdrop of current energy policy objectives, increased awareness of sustainability, high investment costs for new construction and the high number of existing buildings, refurbishment has become much more important in recent years. Refurbishment is usually the preferred solution compared to demolition [Ferreira 2013]. It has also been shown that refurbishment achieves comparable energy consumption levels to new builds, e.g., by implementing the EnerPHit standard.

However, the implementation of a building envelope refurbishment can be complex, as buildings usually have a high degree of variability resulting from individual building geometry, degree of utilization, application, climatic requirements and regional building regulations. As a result, the variety of potential refurbishment solutions that are more or less suitable for the object under consideration also varies [Kamari 2021]. As planners cannot map and compare all solutions, it cannot be ruled out that a suboptimal system selection will be made for the refurbishment measure due to this system diversity. This can have a negative impact on energy performance, user comfort and costs.

It has been shown that key decision-making processes regarding basic system selection usually take place in the early refurbishment phase, when not all relevant information is available. This is where the need for suitable tools to support decision-making arises [Ferreira 2013]. Some software-supported decision-making aids already exist in this respect, but these are mostly geared towards detailed planning rather than early design (cf. [Flourentzou 2002, Kamari 2021, Lanzarote 2021, Wittchen 2000]). In order to provide planners and users with the best possible support in the early design phase and thus ensure the best possible achievement of defined objectives (e.g., energy efficiency), appropriate support solutions are required to handle this variability of potential system solutions. The first step in the renovation process is therefore to define the objectives and criteria, as all subsequent phases are aligned with these strategic and important aspects [Nielsen 2016].

Sustainable refurbishment not only promises to reduce energy requirements, pollutant emissions and waste, but can also help to improve the quality of the property for users, particularly in the areas of air quality and comfort [Genre 2000]. The location proves to be decisive for the choice of refurbishment concept. This is because, depending on the location, there are different climatic conditions, which are reflected in particular in the design of the insulation thickness and thus the costs. Recording information on the building typology, degree of utilization and orientation also provides initial impressions for describing and classifying the existing building situation, as do the age and status of the building technology.

As the objectives of building owners can vary greatly, these must be taken into account when selecting the system solution. These include requirements for energy efficiency (e.g., EnerPHit standard), high comfort and low investment costs. In the context of the objective, the target criterion of energy efficiency also proves to be important, as numerous system solutions also allow the integration of renewable energies, e.g., photovoltaics and/or solar thermal energy in the building façade (e.g. [Torres 2021]). In the context of sustainability, the use of wood as a building material in particular is proving to be a future-oriented, environmentally friendly resource (see [Sandberg 2016]). In addition, the duration of the refurbishment measure is also relevant for the system selection. For reasons of cost and user comfort, it is preferable to let residents live in the existing building. In order to reduce disruptive interventions during refurbishment to a minimum, concepts of serial refurbishment with prefabricated elements have proven themselves in practice. The situation is similar when it comes to accessibility. The dimensions of the façade elements are usually linked to access options for transport vehicles and the possibility of erecting cranes.

The tool development DeSuTo (Decision Support Tool) from outPHit is currently limited to the renovation of the building envelope of residential buildings. In addition, the number of integrated solutions is currently still very limited and restricted to concepts from the literature. Modular expandability ensures the cyclical addition of solutions in the future. To ensure this expandability, a software solution with a standardized data structure was used. This is the only way to ensure long-term usability in the software. The effectiveness of a measure is measured in particular by the resulting energy demand. To do this, it is necessary to record relevant building information and carry out an energy assessment. The PHPP passive house project planning package is used for the assessment. Users work through a list of questions in order to obtain the necessary information for the PHPP and thus generate a sufficiently accurate model of the existing building and at the same time guarantee a high level of usability.

As hardly any detailed information is available during the early building design phase, DeSuTo relies on supplementing missing information with statistically validated data. If a necessary information field can be used by the user, missing information is compensated for by comparing it with statistically validated data from TABULA (Typology Approach for Building Stock Energy Assessment [Loga 2016]) and districtPH. TABULA comprises a database of around 500 buildings from various countries. Questions in the decision tool are used to classify and assign the object under consideration to the TABULA building classes using categories such as building size, age class and location. Missing information on constructions, u-values and heating systems is also obtained. In order to reduce the number of potential variants and thus reduce the calculation time, the question structure already excludes individual concepts. For example, individual systems are defined by the required fixing points, installation conditions and module size. Questions are therefore used to identify potential fixing points and the capacity of existing supporting structures. In addition to site accessibility, these also provide information about possible module sizes for prefabricated façade elements. To achieve a high level of user-friendliness, the support tool was implemented as a web application Decision Support Tool.

The tool provides users with an initial assessment for a potential refurbishment concept. The output includes a brief description of the concept, a link to the relevant literature source and a compact overview of the most important energy performance indicators, which come from the PHPP working in the background.

In addition to the current limitation to a few concepts in literature and residential buildings, the literature emphasizes that social aspects, aesthetics and economic aspects in particular also have a high influence on the system selection and implementation of a refurbishment measure (see [Pombo 2016]). Integrating these criteria would ensure a holistic view and evaluation of refurbishment concepts, which is why this should be the subject of future research and development activities.

As buildings are constructed and used in very different ways, hardly any two refurbishment projects are the same. This can lead to complex decision-making processes when selecting a refurbishment solution. The wrong decisions can lead to the optimal energy saving potential not being achieved. Support tools, such as the DeSuTo program presented here, are used to ease challenges in the selection of suitable renovation solutions for residential buildings. The results of this new program can provide a basis for subsequently planning the next steps with energy consulting experts.

The tool is available at the following link: Decision Support Tool.

[Ferreira 2013] Ferreira, J.; Duarte Pinheiro, M.; de Brito, J.: Refurbishment decision support tools: A review from a Portuguese user’s Perspective, Construction and Building Material, 49, pp. 425-447, 2013.

[Flourentzou 2002] Flourentzou, F.; Genre, J. L.; Roulet, C.-A.: TOBUS software — an interactive decision aid tool for building retrofit studies. Energy and Buildings, 34(2), pp. 193-202, 2002.

[Genre 2000] Genre, J.-L.; Flourentzos, F.; Stockli, T.: Building refurbishment: habitat upgrading, Energy and Buildings, 31(2), pp. 155-157, 2000.

[Gilani 2021] Gilani, G., Hosseini, S. M. A., Pons-Valladares, O., de la Fuente, A.: An enhanced multi-criteria decision-making approach oriented to sustainability analysis of building facades: A case study of Barcelona, Journal of Building Engineering, Bd. 54, p. 104630, 2021.

[Kamari 2021] Kamari, A.; Kirkegaard, P. H.; Schultz, C. P. L.: PARADIS - A process integrating tool for rapid generation and evaluation of holistic renovation scenarios, Journal of Building Engineering, 34, pp. 101944, 2021.

[Lanzarote 2021] Lanzarote, B. S., Escudero, C. I. J., Almela, D. S., Clausell, J. R.: Decision-Making Aid Tool to Support Renovation of Buildings with Industrialised All-in-One Technology Solutions, Environmental Sciences Proceedings, 11(1), p. 4, 2021.

[Loga 2016] Loga, T., Stein, B., Diefenbach, N.: TABULA building typologies in 20 European countries—Making energy-related features of residential building stocks comparable, Energy and Buildings, 132, pp. 4-12, 2016.

[Nemry 2010] Nemry, F., Uihlein, A., Colodel, C. M., Wetzel, C., Braune, A., Wittstock, B., Hasan, I., Kreißig, J., Gallon, N., Niemeier S., Frech, Y.: Options to reduce the environmental impacts of residential buildings in the European Union—Potential and costs, Energy and Buildings, Bd. 42, Nr. 7, pp. 976-984, 2010.

[Nielsen 2016] Nielsen, A. N.; Jensen, R. L.; Larsen, T. S.; Nissen, S. B.: Early stage decision support for sustainable building renovation – A review. Building and Environment 103, pp. 165-181, 2016.

[outPHit D4.4] Hammes, S., Pfluger, R.: outPHit D4.4 Development of a suitable tool for the selection of renovation concepts, 2024 [Pombo 2016] Pombo, O., Rivela, B., Neila, J.: The challenge of sustainable building renovation: assessment of current criteria and future outlook, Journal of Cleaner Production, 123, pp. 88-100, 2016.

[Sandberg 2016] Sandberg, K.; Orskaug, T.; Andersson, A.: Prefabricated wood elements for sustainable renovation of residential building façades. Energy Procedia 96, pp. 756-767, 2016.

[Torres 2021] Torres, J., Garay-Martinez, R., Oregi, X., Torrens-Galdiz, J., Uriarte-Arrien, A. Pracucci, A., Casadei, O., Magnani, S., Arroyo N., Cea, A.: Plug and Play Modular Façade Construction System for Renovation for Residential Buildings, Buildings, Bd. 11, Nr. 9, p. 419, 2021.

[Wittchen 2000] Wittchen, K. B.; Aggerholm, S.: Calculation of building heating demand in EPIQR, Energy and Buildings 31(2), pp. 137-141, 2000.

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