LETTER • OPEN ACCESS Role of temporary thermostat adjustments as a fast, low-cost measure in reducing energy imports To cite this article: N Vulic et al 2022 Environ. Res. Commun. 4 121007   View the article online for updates and enhancements. You may also like Comparative modeling of cost-optimal energy system flexibility for Swedish and Austrian regions Érika Mata, Nicolas Pardo Garcia, Demet Suna et al. - To assessment of heat saving in automation of buildings heating systems V.I. Panferov and S.V. Panferov - Over- and underconsumption of residential heating: Analyzing occupant impacts on performance gaps between calculated and actual heating demand Anders Rhiger Hansen and Kirsten Gram- Hanssen - This content was downloaded from IP address 147.86.218.85 on 04/02/2026 at 08:30 https://doi.org/10.1088/2515-7620/acacef /article/10.1088/2753-3751/ad3191 /article/10.1088/2753-3751/ad3191 /article/10.1088/2753-3751/ad3191 /article/10.1088/1757-899X/687/4/044017 /article/10.1088/1757-899X/687/4/044017 /article/10.1088/1742-6596/2654/1/012062 /article/10.1088/1742-6596/2654/1/012062 /article/10.1088/1742-6596/2654/1/012062 /article/10.1088/1742-6596/2654/1/012062 Environ. Res. Commun. 4 (2022) 121007 https://doi.org/10.1088/2515-7620/acacef LETTER Role of temporary thermostat adjustments as a fast, low-cost measure in reducing energy imports NVulic ,MSulzer ,MRüdisüli andKristinaOrehounig Empa, Swiss Federal Institute ofMaterial Science andTechnology, 8600Dübendorf, Switzerland E-mail: natasa.vulic@empa.ch Keywords: building energy demand, energy demand reduction, occupant behavior, energy transition Abstract Efforts to combat climate change involve long-termplans to reduce the energy demand and increase the share of locally generated renewable energy. However, a sudden change in the geopolitical situationmay require an evenmore rapid response to reduce energy imports through energy- efficiency improvements. In the building sector, retrofits to the building envelope and heating systems are effective, yet time- and cost-intensive to improve energy efficiency. A fast, low-costmeasure to address this need is to lower the temperature set-points in building heating systems towithin comfortable limits. Here, we show the impact of reducing the temperature set-point by 1 °Con heating demand at different scales—building, regional, and national—using demand simulation of 240 Swiss building archetypes and clustering-based upscalingmethods.We demonstrate a nearly 6% reduction in the residential space heating demand at the national level, about a third of which ismet with natural gas.More importantly, the presented approach highlights potential implications of the proposedmeasure across a national residential building stock, considering differences in climate and building archetypes, as well as their spatial distribution. 1. Introduction In response to the climate crisis, countries around theworld have drafted long-term energy strategies, highlighting their commitment to reaching carbon neutrality. These include a shift to renewable energy, a reduction in energy demand in buildings, and amove to sustainablemobility. The strategies often feature a slow and steady transition overmultiple decades. The EuropeanUnion approved the EuropeanGreenDeal to guide Europe’s transition to carbon neutrality by 2050 [1]. Similarly, the Swiss Energy Strategy 2050 proposes the necessary steps to reach the net zero emissions target, whilemaintaining the security and cost-effectiveness of its energy supply [2]. Sudden geopolitical shiftsmay, however, require an even faster response to reduce the energy dependency [3] before long-term solutions can be implemented. Demand reduction is an important aspect in decreasing that dependency. In the short term, this would also require collective action on the side of individuals/homes. Given the high reliance on fossil fuels tomeet heating demand, reducing the heating temperature levels in buildings—and, if possible,maintaining it within comfortable limits—is one potential way to achieve that. Both REPowerEU [4] and IEA 10-point plan [5] include thermostat adjustment as a temporary measure to reduce demand. Assessing the potential impact of thermostat adjustment on demand across a national residential building stock—while considering differences among building archetypes, as well as spatial variations in the building stock and climate—can provide important insight regarding the effectiveness of the proposedmeasure. Here we use 240 residential building archetypes representing the Swiss building stock (approx. 1.8million buildings according to official national statistics [6]) to evaluate the impact of the occupant—setting the temperature setpoint 1 °C lower—on reducing the space heating energy demand. Clustering-based upscaling methods are then used to obtain the demand reduction potential at different scales. Themethodology is briefly OPEN ACCESS RECEIVED 17August 2022 REVISED 30November 2022 ACCEPTED FOR PUBLICATION 19December 2022 PUBLISHED 30December 2022 Original content from this workmay be used under the terms of the Creative CommonsAttribution 4.0 licence. Any further distribution of this workmustmaintain attribution to the author(s) and the title of thework, journal citation andDOI. © 2022TheAuthor(s). Published by IOPPublishing Ltd https://doi.org/10.1088/2515-7620/acacef https://orcid.org/0000-0003-2525-8166 https://orcid.org/0000-0003-2525-8166 https://orcid.org/0000-0003-2094-2460 https://orcid.org/0000-0003-2094-2460 https://orcid.org/0000-0001-6491-7641 https://orcid.org/0000-0001-6491-7641 mailto:natasa.vulic@empa.ch https://crossmark.crossref.org/dialog/?doi=10.1088/2515-7620/acacef&domain=pdf&date_stamp=2022-12-30 https://crossmark.crossref.org/dialog/?doi=10.1088/2515-7620/acacef&domain=pdf&date_stamp=2022-12-30 http://creativecommons.org/licenses/by/4.0 http://creativecommons.org/licenses/by/4.0 http://creativecommons.org/licenses/by/4.0 outlined in section 2, followed by the presentation and discussion of results in section 3. Finally, the summary of themainfindings, including the limitations and additional considerations, are presented in section 4. 2.Methodology Figure 1 below outlines the processflow for estimating the demand reduction potential of the lowered temperature setpoint. The simplified representation of the Swiss building stock for bottom-up energy demand analysis is obtained using a grouping and clustering approach presented in [7]. The building stock isfirst grouped by building type, building age and climate region, and then further clustered based on spatial and geometric characteristics (building compactness, size, and density of the surrounding), resulting in 240 residential building archetypes. Their heating energy demands are simulated in theCombined Energy Simulation andRetrofit in Python (CESAR-P) [8, 9], which is based on the EnergyPlus software [10]. In the validated reference case, heating temperature setpoints are set according to the Swiss SIA standard for single- family andmulti-family buildings (21 °C) [9, 11]. Demand simulations are performed for theweather year 2016. The clustering-based upscaling approach is then used to obtain the demands at different scales (e.g. building, regional, and national). In this paper, we include past building energy retrofits to themodel based on the distribution from [12]. Consequently, themodelled national space heating demand results are brought in close agreement to the national statistics [13] and a number of other building energy simulation studies in Switzerland [14–16] (see figure A1). Themodel is then used to estimate the heating demand reduction potential of the lowered temperature setpoint at different scales. In the new reduced setpoint case, we lower the heating temperature setpoint by 1 °C (while keeping the set-back temperature unchanged). The change in temperature setpoint has only aminor impact on the comfort perception of people, which can be easily estimated using the predictedmean vote (PMV) method.While steady-state laboratory experiments onwhich themethod is basedmay not be quite comparable to those in residential buildings (and can vary according to time-of-year, room type, clothing, activity, etc.) [17], the PMVmethod provides a simpleway to assess the relative impact of the proposed thermostat adjustment. To estimate the predicted percent of dissatisfied people (PPD), we use air temperature in the assessmentwithin the CBEThermal Comfort Tool [18]. Keeping all other parameters constant, reducing the temperature setpoint by 1°—from21° to 20 °C—the PMVmethod estimates that the PPDwill increase from13 to 21% (see appendix B, Table B1, for parameter settings). This change, however, does not take into account thatwithin residential environments occupantsmay have various possibilities to adapt themselves to reach a desired thermal comfort (e.g. clothing adjustment). The reference and the reduced setpoint cases are compared at different levels—building, city, regional, and national—to estimate the demand reduction potential. To estimate the potential impact on the natural gas use, the obtained relative (percent) reduction in demand is applied to the breakdown of energy carriers supplying residential space heating demand for the year 2020 [19]. 3. Results and discussion Herewe outline the impact of the reduced temperature setpoint from the building to the national level, including its potential in reducing the overall natural gas use. Figure 2 shows the impact of the reduced temperature set-point for the upscaled (national) building stock according to different building type/age groups. The results are based on the stock-level evaluation of all 240 simulated archetypes, accounting for the variance within each category (24 archetypes/category). Variations Figure 1.Process flow. 2 Environ. Res. Commun. 4 (2022) 121007 within building type/age groups are included in the appendix (figureC1), and arisemainly due to differences in the climate regions. The annual specific heating demands and the overall trends across building type/age groups for the reference case are comparable to other Swiss studies [15, 16, 20, 21], with differences arising fromweather and input data, as well consideration of whether past retrofits are considered in themodel. Total heating demands obtained for each category are normalized to their total energy reference area (i.e. heatedfloor area of buildings, assumed as 90%of the building floor area) to obtain the average annual specific heating demands. The width of the bars relates to their proportional representation in the national building stock in terms of energy reference area (see figureC2 for the corresponding values). Demand reduction is shownusing the hatched areas. For each building type/age category, change in the annual specific heating demand is represented by the height of the hatched region (and indicated as percent reduction); the area of the hatched region relates to the total demand reduction potential within the national building stock (indicated in parentheses), which is a factor of the total energy reference area in any specified group and its correspondingmagnitude of demand reduction. The oldest buildings (<1945) show the lowest percent reduction in their annual specific heating demand (5.4% forMFHand 5.2% for SFH) due to their poor thermal performance, but an overall higher absolute specific demand reduction. Taking into account both their large representationwithin the national building stock (in terms of energy reference area, illustrated by the barwidth) and themagnitude of demand reduction, this building age category has the highest heating demand reduction potential (22.6% forMFHand 18.5% for SFH). On the other hand, the newest buildings (>2010) show the highest percent reduction in their annual specific heating demand (12.6% forMFHand 8.9% for SFH)due to their better thermal performance, but an overall lower absolute reduction of their specific demand. A combination of lower relative demand reduction and smaller representation in the building stock (illustrated by the barwidth, approximately 6.5% for SFH and MFHcombined), this building age category contributes onlymarginally to the national reduction in demand (1.8% forMFHand 0.8% for SFH). Infigure 3, we show the spatial distribution of demand reduction potential across different cities and regions (cantons) in Switzerland, by taking into account the impact of climate zones (see figure C3 in the Appendix), as well as the spatial distribution of the building stock and their attributes (i.e. represented archetypes). In (a) the impact of the reduced temperature setpoint is shown for the nation’s five largest cities with similar relative reductions in demand.While the building stock of each city ismodeled separately, similar relative reductions in demand appear related to belonging to the same climate zone (’Large urban agglomerates’) and a similar breakdownof building types (see figure C2 in the appendix). Absolute differences between cities can be attributed to the differences in the total building energy reference areas within theirmunicipal boundaries, which in the case of similar urban densities, correlates well to their respective populations. Together theywould contribute to 8.2%of the nation’s space heating demand reduction potential. Infigure 3(b), wemainly observe the impact of weather on heating demand reduction in different regions, and to a smaller extent, building stock composition (for example, relative proportions of building types). The percent reduction across different cantons varies between 5.3 and 6.9%,with higher changes in demand observed inwarmer regions, especially in the south. Infigure 3(c), the contribution to the national reduction in demand relates the absolute reduction in each canton to the national reduction in demand, showing the Figure 2.The average annual specific heating demand by building type and age for the reference (hatched) and the reduced temperature set-point (solid) cases. The values are calculated taking into account proportional representation in the national building stock of the 240 clusters containedwithin the building age/type categories shownhere (24 archetypes/category). Relative demand reduction and its contribution to the national reduction potential (in parentheses) are shown for each category. Thewidth of the bars relates to their proportional representation in the national building stock in terms of the energy reference area (see figure C2 for the corresponding percent distribution). 3 Environ. Res. Commun. 4 (2022) 121007 implication of weather and regional buildings stock size, which correlates to the differences in population. The percent reduction varies between about 0.3 and 14.6%,with the highest potential contribution to reducing demand concentratedwithin themost populous cantons (Zürich andBern). Infigure 4, the derived reduction of 5.9% in residential space heating demand at the national scale (useful energy) is uniformly applied to the energy carrier breakdown of residential space heating demand—final energy demand by energy carrier type—for the year 2020. In other words, a 1:1 percent reduction is assumed for the useful andfinal energy demands. This results in a 6.7% reduction in residential natural gas demand for space heating, and a 2.2% reduction in total (national)natural gas demand.Our results are in close agreement with those obtained by IEA, which estimates a saving of 10 billion cubicmeter (bcm) per 1 °C [5]. Taking into account the natural gas use for space heating in EUbuildings [22], 10 bcm corresponds to the approximate reduction of 6%–7% (see appendixD, TableD1). Aside from the stated higher temperature setpoint of 22 °C, the details of the IEAmethod for estimating gas reduction in the EUhave not been provided in the report.We can only assume that they are based on heuristic approaches as opposed to complex building stockmodels. Due to the similarities between Switzerland and the EU-27 in terms of the building stock age [23] and the represented Figure 3.A spatial analysis of (a) change in demand and contribution to reduction in national demand (in parentheses) for the nation’s five largest cities, (b) change in demand and (c) contribution to reduction in demand across different regions in Switzerland. Figure 4. (a)Breakdown in heating energy demand at the national level (2020) for the reference and the reduced temperature set-point scenario showing a potential reduction of 6.7% in natural gas consumption. (b)Estimated impact on the national natural gas consumption arsing from reduction in heating demand of 2.2%. 4 Environ. Res. Commun. 4 (2022) 121007 climate regions (with the exception of theMediterranean), we expect that our results would be reasonably comparable. Nevertheless, EUs higher reliance on natural gas for space heating (50%versus 30%) [24]would result in higher relative savings from the proposedmeasure. The Swiss building and housing registry [6] also suggests that older buildings aremore likely to have an oil or natural gas boiler compared to newer ones.However, this data is no longer regularly updated to reflect the change of heating systems. Nevertheless, a detailed analysis of the Swiss Cantonal EnergyCertificate for Buildings, which provides a representative sample of the Swiss building stock in terms of building type/age distribution, andmore recent data on the installed heating systems [25], showed that whilemost old buildings have new boilers installed, they largely remain fossil fuel based (74% for buildings built before 1990, where the retrofit was conducted after 1990). At the same time, national statistics show a trend of decreased use of heating oil and an increased use of natural gas tomeet space heating demand (from2000 to 2020, -50.5%+32.6%, respectively) [19]. In newer buildings (built after 2000), heat pumps are themain space heating technology [6]. As a result, older buildings are expected to contributemore to reducing fossil fuel use compared to newer buildings. 4. Conclusion In this studywe estimated the heating demand reduction potential of lowering the temperature setpoint in residential buildings by 1 °Cat different scales—building, regional, and national—using demand simulation of Swiss building archetypes and clustering-based upscalingmethods. Based on the results, wemake the following key takeaways: • Newer buildings show the highest percent reduction in demand (9.9% for SFH, 12.6% forMFH). This is due to the their higher energy efficiency (i.e better insulation of the building envelope that lowers the heating threshold for the operation of the heating system). As these buildings have both lower absolute demand reduction compared to older buildings and represent a small portion of the building stock (approx. 6.5%of the national energy reference area), they contribute to only 2.6%of the national demand reduction potential. • Oldest buildings show the lowest relative change in demand (5.2% for SFH and 5.4% forMFH). However, due to their large number (approx. 33%of the national energy reference area) and their relatively high heating demand, they could contribute over 40% to the national reduction in demand. • Older buildings aremore likely to have oil and gas boiler installed (74%of buildings built before 1990 according one of the recent estimates [25] compared to 64%based on national statistics [19]), and can therefore contributemore in reducing fossil fuel use. In newer buildings (built after 2000), heat pumps are the main space heating technology [6]. • Different regions have varying levels of relative reduction in demand (from5.4% to 6.8%) due to a combination of climate variations and the building stock composition (i.e. archetype representation).When contribution to the absolute demand reduction is assessed (where changes among regions arise due to the distribution of the building stock), together they reach an estimated demand reduction potential of 5.9% (i.e. overall savings of residential buildings at the national scale). • Estimated reduction in total natural gas demand arising from temperature setpoint reduction in residential buildings (where residential space heating demand comprises approximately one third of the Swiss natural gas demand) is 2.2%. This analysis shows a reasonable impact on the space heating demand by reducing the setpoint. However, although absolute energy savings are onlymoderate, such a reduction of the temperature setpoint can immediately be done at almost no additional cost. Therefore, itmay still be an adequate andworthwhilemeasure as a response to short-term fossil fuel supply issues and/or country’s dependency on certain fuel supply sources. However, this is insufficient demand response if a complete offset of the Russian gas imports (47%of the Swiss gas import [26]) is considered: 1 degree thermostat adjustment would only be able to cover 14%of the demand for space heating, and 4.7%of total national demand. In themid- to long-run, additionalmeasures will be needed to reduce the overall energy demand and provide greater flexibility in addressing short-term shortages. These are discussed in the further considerations section below (section 4.2). 4.1. Limitations Our simulation is based on standard use conditions (i.e. a reference temperature setpoint of 21 °C). In reality, the temperature setpoints can vary between buildings, and the average temperature setpointmay bemuch 5 Environ. Res. Commun. 4 (2022) 121007 higher (above 22 °Caccording to IEA [5]) or lower, and can depend on time of year [17], as well as a combination of building energy efficiency and socioeconomic factors [27]. This could then have a significant impact on the effectiveness of thermostat adjustment on the national scale. Additionally, differences in specific heating energy demand among similar buildings (i.e. belonging to the same archetype) can go beyond differences in the temperature setpoints. The heating demand is particularly sensitive to the variations in ventilation rate [28], which is not considered here. To estimate the impact of thermostat adjustment on natural gas use, we apply a uniform reduction across all energy carriers based on the building stock results due the limitation on available data relating to heating technology and energy carrier (e.g. according to building type/age). The available building level data is not up- to-date, especially for older buildings inwhich heating systemsmay have already been replaced. 4.2. Further considerations Decreasing the temperature setpoint in building heating systems by a small amount (1 °C) is oneway of reducing demand, while having aminor impact on the perceived comfort of occupants. However, optimizing the use of energy forwhen andwhere it is neededmost can further reduce demand.Most thermostats aremanual, non- programmable spring loaded thermostats oftenwithout a temperature readout. They are set to a single setpoint and are often running regardless of occupancy and need. If thermostats in older buildings are replaced by simple rule-based thermostats (with predefined heating schedules set by the occupant) or smart thermostats (that dynamically adjust the setpoints according to theweather forecast and occupancy behaviourwhile keeping the desired comfort [29]), large energy savings can be achieved (−20%–30% in some cases [30]). In the long run, energy-related building retrofits and updates to the heating systemswill be required to increase the efficiency of energy use and reduce dependence on fossil fuels. Data availability statement The data that support thefindings of this study are available upon reasonable request from the authors. AppendixA. Validation of the results on the national scale Figure A1.Comparison of the energy demandmodeling results to the national statistics [13] and literature [14–16], with the results for the residential demand highlighted. Figure adapted from [7]. 6 Environ. Res. Commun. 4 (2022) 121007 Appendix B. Assumptions in the predicted percentage of dissatisfied (PPD) The assessment of PPD is performed using theCBEThermal Comfort Tool [18] under the EN16 798-1:2019 standard. Air temperature is used in the assessment, with the assumption that air temperature andmean radiant temperature are equal. The air speed, relative humidity,metabolic rate, and dynamic clothing insulation are held constant in the calculation, and are shown in the table below. AppendixC. Archetype, climate, and building stock variations Table B1.Assumptions used in PPD calculations. Parameter Value (description) Air speed 1 m/s Relative humidity 50% Metabolic rate 1met (seated, quiet) Dynamic clothing insulation 1 clo (typical winter indoor clothing) FigureC1.Variations in the annual specific heating demand for the building archetypes across the different climates. 7 Environ. Res. Commun. 4 (2022) 121007 FigureC2. Percentage of the energy reference area for the full national building stock and itsfive largest cities. The cities show similarities in the building type/age distribution. FigureC3. Swiss climate regions used in the grouping of building archetypes. The average annual temperatures are shown in parentheses for each region.Details on creating theweather files for each climate region is described in [7] AppendixD. Comparison to IEA values: back of envelope calculation The percentage change in energy demand (ΔEdemand), corresponding to 10 bcm reduction in natural gas use [5], is approximately: E 100 6.9%demand PJ PJ 360 5226 **D = = -- . *Conversion factor: 1 billion cubicmeter (bcm)natural gas= 36 PJ [31]. ORCID iDs NVulic https://orcid.org/0000-0003-2525-8166 MSulzer https://orcid.org/0000-0003-2094-2460 KristinaOrehounig https://orcid.org/0000-0001-6491-7641 TableD1.Natural gas use in buildings (EU) in PJ. Residential space heating (EU-28) 4385 [22] Residential space heating (UK) 769 [22] Service/tertiary space heating (EU-28) 1868 [22] Service/tertiary space heating (UK) 258 [22] Total (residential+ tertiary, EU-28) 6253 Total (residential+ tertiary, EU-27) 5226 8 Environ. Res. Commun. 4 (2022) 121007 https://orcid.org/0000-0003-2525-8166 https://orcid.org/0000-0003-2525-8166 https://orcid.org/0000-0003-2525-8166 https://orcid.org/0000-0003-2525-8166 https://orcid.org/0000-0003-2094-2460 https://orcid.org/0000-0003-2094-2460 https://orcid.org/0000-0003-2094-2460 https://orcid.org/0000-0003-2094-2460 https://orcid.org/0000-0001-6491-7641 https://orcid.org/0000-0001-6491-7641 https://orcid.org/0000-0001-6491-7641 https://orcid.org/0000-0001-6491-7641 References [1] EuropeanComission 2019The EuropeanGreenDeal PublicationsOffice of the EuropeanUnion https://eur-lex.europa.eu/legal- content/EN/TXT/?uri=COM:2022:108:FIN [2] Bundesversammlung der Schweizerischen Eidgenossenschaft 2021 730.0 Energiegesetz vom30. september 2016 (EnG) https://www. fedlex.admin.ch/eli/cc/2017/762/de [3] Bundesamt für Statistik 2021Monet2030: Energieabhängigkeit online https://www.bfs.admin.ch/bfs/de/home/statistiken/ nachhaltige-entwicklung/monet-2030/indikatoren/energieabhaengigkeit.html [4] EU2022REPowerEU: Joint european action formore affordable, secure and sustainable energy PublicationsOffice of the European Union https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2022:108:FIN [5] IEA 2022A 10-point plan to reduce the EuropeanUnionʼs reliance onRussian natural gas https://www.iea.org/reports/a-10-point- plan-to-reduce-the-european-unions-reliance-on-russian-natural-gas [6] BFS 2016Gebäude- undWohnungsstatistik 2015:Wohnungen nachHeizungsart sowie Energieträger derHeizung undBauperiode https://www.bfs.admin.ch/bfs/de/home/statistiken/bau-wohnungswesen/gebaeude/energiebereich.assetdetail.1642796.html [7] Eggimann S, Vulic N, RüdisüliM,Mutschler R,Orehounig K and SulzerM2022Energy Build. 258 111844 [8] WangD, Landolt J,Mavromatidis G,Orehounig K andCarmeliet J 2018Energy Build. 169 9–26 [9] Orehounig K, Fierz L, Allan J, Eggimann S, VulicN andBojarski A 2022CESAR-P: A dynamic urban building energysimulation tool J. Open Source Software 7 (78) 4261 [10] CrawleyDB, Lawrie LK,Winkelmann FC, BuhlWF,HuangY J, PedersenCO, StrandRK, LiesenR J,WitteM J andGlazer J 2001 Energy and buildings 33 319–31 [11] SIA 2006 SIA 2024, Standard-Nutzungsbedingungen für die Energie-undGebbäude- technik Tech. rep. [12] Streicher KN, Padey P, ParraD, BürerMC andPatelMK2018Energy Build. 178 360–78 [13] SFOE2017Analyse des schweizerischen Energieverbrauchs 2000-2016 nachVerwendungszwecken https://www.bfe.admin.ch/bfe/de/ home/versorgung/statistik-und-geodaten/energiestatistiken/energieverbrauch-nach-verwendungszweck.html [14] Siller T, KostM and ImbodenD 2007Energy Policy 35 529–39 [15] Kirchner A, BredowD, Ess F,Grebel T,Hofer P, Kemmler A and Struwe J 2012Bundesamt für Energie (BFE), Bern, Switzerland [16] Streicher KN, Padey P, ParraD, BürerMC, Schneider S and PatelMK2019Energy Build. 184 300–22 [17] Peeters L, deDear R,Hensen J andD’haeseleerW2009Appl. Energy 86 772–80 [18] Tartarini F, Schiavon S, Cheung T andHoyt T 2020 SoftwareX 12 100563 [19] SFOE2021Analyse des schweizerischen Energieverbrauchs 2000 - 2020 nachVerwendungszwecken https://www.bfe.admin.ch/bfe/de/ home/versorgung/statistik-und-geodaten/energiestatistiken/energieverbrauch-nach-verwendungszweck.html [20] Girardin L,Marechal F, DubuisM,Calame-DarbellayN and Favrat D 2010Energy 35 830–40 [21] Schneider S,Hollmuller P, Le Strat P, Khoury J, PatelM and Lachal B 2017 Frontiers in Built Environment 3 53 [22] HRE:Heat Roadmap Europe 2017Heating and cooling: facts and figures https://heatroadmap.eu/heating-and-cooling-energy- demand-profiles/ [23] EuropeanCommission EUbuildings factsheets: Building stock characteristics 2014 https://ec.europa.eu/energy/eu-buildings- factsheets_en [24] BagheriM,Mandel T, Fleiter T, Viegand J, Naeraa R, Braungardt S andKranzl L 2022Renewable SpaceHeatingUnder the Revised Renewable EnergyDirective : ENER/C1/2018-494 : Description of TheHeat Supply Sectors of EUMember States SpaceHeatingMarket Summary 2017PublicationsOffice of the EuropeanUnion https://data.europa.eu/doi/10.2833/256437 [25] Cozza S, Chambers J, Geissler A,WesselmannK,Gambato CA, BrancaG, CadonauG, Arnold L and PatelM2019 Gapxplore: Energy performance gap in existing, new, and renovated buildings learning from large-scale datasets https://www.aramis.admin.ch/Default? DocumentID=51025&Load=true [26] Gaz Energie Verband der schweizerischenGasindustrie Statistik 2021 https://gazenergie.ch/fileadmin/user_upload/e-paper/GE- Jahresstatistik/VSG-Jahresstatistik-2021.pdf [27] Galvin R and Sunikka-BlankM2016Energy 95 415–24 [28] Khoury J, Alameddine Z andHollmuller P 2017Energy Procedia 122 217–22 [29] Oldewurtel F, Parisio A, Jones CN,GyalistrasD, GwerderM, StauchV, LehmannB andMorariM2012 Energy Build. 45 15–27 [30] Bünning F,Huber B, Schalbetter A, Aboudonia A, de BadynMH,Heer P, Smith R S and Lygeros J 2022Appl. Energy 310 118491 [31] BPApproximate conversion factors: Statistical review ofworld energy 2021 https://www.bp.com/content/dam/bp/business-sites/ en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-approximate-conversion-factors.pdf 9 Environ. Res. Commun. 4 (2022) 121007 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2022:108:FIN https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2022:108:FIN https://www.fedlex.admin.ch/eli/cc/2017/762/de https://www.fedlex.admin.ch/eli/cc/2017/762/de https://www.bfs.admin.ch/bfs/de/home/statistiken/nachhaltige-entwicklung/monet-2030/indikatoren/energieabhaengigkeit.html https://www.bfs.admin.ch/bfs/de/home/statistiken/nachhaltige-entwicklung/monet-2030/indikatoren/energieabhaengigkeit.html https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2022:108:FIN https://www.iea.org/reports/a-10-point-plan-to-reduce-the-european-unions-reliance-on-russian-natural-gas https://www.iea.org/reports/a-10-point-plan-to-reduce-the-european-unions-reliance-on-russian-natural-gas https://www.bfs.admin.ch/bfs/de/home/statistiken/bau-wohnungswesen/gebaeude/energiebereich.assetdetail.1642796.html https://doi.org/10.1016/j.enbuild.2022.111844 https://doi.org/10.1016/j.enbuild.2018.03.020 https://doi.org/10.1016/j.enbuild.2018.03.020 https://doi.org/10.1016/j.enbuild.2018.03.020 https://doi.org/10.21105/joss.04261 https://doi.org/10.1016/S0378-7788(00)00114-6 https://doi.org/10.1016/S0378-7788(00)00114-6 https://doi.org/10.1016/S0378-7788(00)00114-6 https://doi.org/10.1016/j.enbuild.2018.08.032 https://doi.org/10.1016/j.enbuild.2018.08.032 https://doi.org/10.1016/j.enbuild.2018.08.032 https://www.bfe.admin.ch/bfe/de/home/versorgung/statistik-und-geodaten/energiestatistiken/energieverbrauch-nach-verwendungszweck.html https://www.bfe.admin.ch/bfe/de/home/versorgung/statistik-und-geodaten/energiestatistiken/energieverbrauch-nach-verwendungszweck.html https://doi.org/10.1016/j.enpol.2005.12.021 https://doi.org/10.1016/j.enpol.2005.12.021 https://doi.org/10.1016/j.enpol.2005.12.021 https://doi.org/10.1016/j.enbuild.2018.12.011 https://doi.org/10.1016/j.enbuild.2018.12.011 https://doi.org/10.1016/j.enbuild.2018.12.011 https://doi.org/10.1016/j.apenergy.2008.07.011 https://doi.org/10.1016/j.apenergy.2008.07.011 https://doi.org/10.1016/j.apenergy.2008.07.011 https://doi.org/10.1016/j.softx.2020.100563 https://www.bfe.admin.ch/bfe/de/home/versorgung/statistik-und-geodaten/energiestatistiken/energieverbrauch-nach-verwendungszweck.html https://www.bfe.admin.ch/bfe/de/home/versorgung/statistik-und-geodaten/energiestatistiken/energieverbrauch-nach-verwendungszweck.html https://doi.org/10.1016/j.energy.2009.08.018 https://doi.org/10.1016/j.energy.2009.08.018 https://doi.org/10.1016/j.energy.2009.08.018 https://doi.org/10.3389/fbuil.2017.00053 https://heatroadmap.eu/heating-and-cooling-energy-demand-profiles/ https://heatroadmap.eu/heating-and-cooling-energy-demand-profiles/ https://ec.europa.eu/energy/eu-buildings-factsheets_en https://ec.europa.eu/energy/eu-buildings-factsheets_en https://data.europa.eu/doi/10.2833/256437 https://www.aramis.admin.ch/Default?DocumentID=51025%26Load=true https://www.aramis.admin.ch/Default?DocumentID=51025%26Load=true https://gazenergie.ch/fileadmin/user_upload/e-paper/GE-Jahresstatistik/VSG-Jahresstatistik-2021.pdf https://gazenergie.ch/fileadmin/user_upload/e-paper/GE-Jahresstatistik/VSG-Jahresstatistik-2021.pdf https://doi.org/10.1016/j.energy.2015.12.034 https://doi.org/10.1016/j.energy.2015.12.034 https://doi.org/10.1016/j.energy.2015.12.034 https://doi.org/10.1016/j.egypro.2017.07.348 https://doi.org/10.1016/j.egypro.2017.07.348 https://doi.org/10.1016/j.egypro.2017.07.348 https://doi.org/10.1016/j.enbuild.2011.09.022 https://doi.org/10.1016/j.enbuild.2011.09.022 https://doi.org/10.1016/j.enbuild.2011.09.022 https://doi.org/10.1016/j.apenergy.2021.118491 https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-approximate-conversion-factors.pdf https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-approximate-conversion-factors.pdf 1. Introduction 2. Methodology 3. Results and discussion 4. Conclusion 4.1. Limitations 4.2. Further considerations Data availability statement Appendix A. Appendix B. Appendix C. Appendix D. References