OpenBEERS: A digital platform for urban scale simulation of building energy efficiency

It is urgent that the built environment transitions towards renewable energies and adopts ambitious renovations strategies as both are unavoidable steps towards long term sustainability goals. While extensive research has been focused on the integration of buildings in renewable energy production networks as well as on renovation measures such as thermal insulation and installation of heat-pumps, it is still unclear how these actions perform at scale when combined together. To fill this gap, this paper introduces the digital platform OpenBEERS (Open-data for Building Energy Efficiency, Renovation and Storage) that allows simulation of building renovation and renewable energy deployment scenarios at the urban scale. This web-platform integrates three main components: a database to store geometric and semantic information on the built environment, open source simulation tools for the modeling of future scenarios and present baselines as well as a 3D web-interface for the visualization of the input and output data. The OpenBEERS platform leverages existing Swiss open-data platforms and integrates in this panorama by offering an open-access application programming interface (API) based on open-standards protocols. Advanced physical simulation capabilities are provided by the open-source urban energy modeling software CitySim which allows to perform detailed analysis of long-wave and short-wave radiation fluxes between buildings and their surrounding environment as well as enables precise characterization of convective and conductive heat transfer processes. This tool also allows to estimate the energy consumption due to human activities for both residential and office buildings, decentralized photovoltaic (PV) production potential and enables simulation of complex heating and cooling systems such as heat storage facilities and district heating networks (DHN). To complement these features, our platform also integrates other open-source tools, such as the battery schedule optimizer for residential applications (BASOPRA) software, to further model thermal and electric demand, production and storage at the aggregated scale. To validate the capabilities of our digital platform, and demonstrate the relevance of our approach, we select three communes in the Canton Valais as case studies to conduct a detailed analysis of a wide range of building renovation, renewable deployment and climate scenarios. For these three cases, we perform a detailed data collection campaign to complement the available open-data information, in particular, communal energy use as well as production and storage capacities are gathered and complemented with synthetic data where necessary. We then define realistic scenarios for future energy use, production as well as building renovation and proceed with physical simulation of the cases studies. Based on this predicted data, we estimate PV production, electricity demand as well as heating and cooling requirements for all scenarios. To ensure proper evaluation of the objectives, we develop a comprehensive package of key performance indicators (KPIs) to indicate where efforts should be concentrated and to provide stakeholders with precise data on the combined impact of the different measures. Finally we conclude with a set of concrete guidelines based on our observations.

The platform is still in a testing stage, please report bugs and problems to: david_geissbuhler_idiap_ch (replace underscores by dot or at symbol).

This preliminary study is limited to five municipalities of the State of Valais

Collombey-Muraz
Conthey
Monthey
Sierre
Val-de-Bagnes

Building geometry

The building 3D envelope is derived from the swissBUILDINGS3D 3.0 Beta dataset. The 3D envelopes in the dataset are cut into individual buildings according to the 2D geometry of the building footprint in the Land Registry, as the 3D shape of each building is not always exactly corresponding to the Land Registry entry.

Building construction year

The building construction year is inferred from the Federal Building and Lodging Register (RegBL), (code GBAUP)

GBAUP	Year of Construction
8010	1945
8011	1945
8012	1945
8013	1960
8014	1970
8015	1980
8016	1985
8017	1990
8018	1995
8019	2000
8020	2005
8021	2010
8022	2015
8023	2023

Climate Scenarios and Far Field Obstruction

Radiative forcing IPCC Potsdam Institute for Climate Impact Research, RCP Concentration Calculations and DataFinal Version, background data, acknowledgements and further info.

Surface temperature Time series of global annual mean surface air temperature anomalies (relative to 1986–2005) from CMIP5 concentration-driven experiments IPCC Fifth Assessment Report

Climate data and far field obstructions are gathered from MeteoNorm for each municipality in our study. Climate data for contemporary simulation is based on observational data (2024) while climate data for future simulation is based on predictions. The following climate scenarios are considered:

Climate Scenario	Simulation Years	Radiative Forcing @ 2100 [W m^-2]
Contemporary	2025	-
IPCC RCP2.6	2035, 2040, 2050	2.6
IPCC RCP8.5	2035, 2040, 2050	8.5

Energy and Renovation Scenarios

Energy growth Phases, mechanisms, models and metrics of wind and solar power adoption. a, Mechanisms that affect the formative, growth and saturation phases that define the S-curve of solar and/or wind technology adoption. b–d, Mathematical formalization of this S-curve based on empirical data (grey dots) through logistic (blue) and Gompertz (orange) models (Methods). A. Cherp et al., National growth dynamics of wind and solar power compared to the growth required for global climate targets. Nature Energy (2021).

Energy growth Historical deployment of wind and solar power and growth models for selected countries. A. Cherp et al., National growth dynamics of wind and solar power compared to the growth required for global climate targets. Nature Energy (2021).

Renewable energy production Valais Renewable energy production historical data and forecast for the state of Valais Valais, Terre d’énergies : Ensemble vers un approvisionnement 100% renouvelable et indigène, Vision 2060 et objectifs 2035.

OpenBEERS scenarios rate Simplified renewable deployment and energy renovation ratios model based on logistic S-curves implemented in OpenBEERS.

OpenBEERS scenarios rate

Name	Maximum rate [%/y]	Inflection year	Minimum ratio [%]
EnL	2	2050	70
EnH	3	2040	100
ReL	1	2070	70
ReM	2	2050	80
ReH	3	2040	100

Renewable energy deployment and renovation ratio for all scenarios.

Scenario Name	Climate Scenario	Renewable Deployment	Photovoltaic Installation	Renovation Ratio
Climate:Cotemporary	Cotemporary	Baseline	Roof	Baseline

Infiltration rate

Infiltration rate of the buildings are determined by the year the building was constructed and renovation status.

Year	Infiltration rate	Renovated Infiltration rate
before 1945	1.4	0.3
1945 - 1960	1.3	0.3
1961 - 1970	1.2	0.3
1971 - 1980	1.1	0.3
1981 - 1990	1.0	0.3
1991 - 2000	0.8	0.3
2001 - 2010	0.7	0.3
2011 - 2015	0.7	0.3
2016 and after	0.35	0.3

Glazing

Glazing characteristics of the buildings are determined by the year the building was constructed and renovation status.

Year	Glazing U Value [W/m ²K]	Renovated Glazing U Value [W/m ²K]	Glazing G Value	Glazing ratio
before 1945	2.3	1.2	0.47	0.25
1945 - 1960	2.3	1.2	0.47	0.25
1961 - 1970	2.3	1.2	0.47	0.25
1971 - 1980	2.3	1.2	0.47	0.25
1981 - 1990	2.3	1.2	0.47	0.25
1991 - 2000	2.3	1.2	0.47	0.25
2001 - 2010	1.5	1.2	0.48	0.35
2011 - 2015	1.3	1.2	0.53	0.4
2016 and after	1.2	1.2	0.53	0.4

Walls

The characteristics of the walls are determined year the building was constructed. The following table gives the wall composite id for a given time period. The composites are defined in a table below.

Year	Composite	Renovated Composite	U value [W/m ²K]	Renovated U value [W/m ²K]
before 1945	100	110	-	-
1945 - 1960	101	111	-	-
1961 - 1970	102	112	-	-
1971 - 1980	103	113	-	-
1981 - 1990	104	114	-	-
1991 - 2000	105	115	-	-
2001 - 2010	106	116	-	-
2011 - 2015	107	117	-	-
2016 and after	108	117	-	-

Roofs

The characteristics of the roofs are determined year the building was constructed. The following table gives the U value for roofs surfaces.

Year	Composite	Renovated Composite	U value [W/m ²K]
before 1945	-	-	0.70
1945 - 1960	-	-	0.70
1961 - 1970	-	-	0.65
1971 - 1980	-	-	0.60
1981 - 1990	-	-	0.43
1991 - 2000	-	-	0.31
2001 - 2010	-	-	0.25
2011 and after	-	-	0.22

Floors

The characteristics of the floors are determined year the building was constructed. The following table gives the U value for floor surfaces.

Year	Composite	Renovated Composite	U value [W/m ²K]
before 1945	-	-	1.60
1945 - 1960	-	-	1.50
1961 - 1970	-	-	1.30
1971 - 1980	-	-	1.10
1981 - 1990	-	-	0.68
1991 - 2000	-	-	0.49
2001 - 2010	-	-	0.35
2011 and after	-	-	0.25

Ground

The following table describe physical parameters of ground surfaces.

Type_name	Composite	Shortwave reflectance	K Factor
Road surface	400	0.08	0.0
Green surface	401	0.3	0.43
Water surface	402	0.3	1.0
Rock surface	403	0.21	0.0

Layered Composites

The following table describe the composition of layered composites. Renovated composites will be used for future simulations.

id	Name	Layer Name	Thickness [m]	Density [kg m^-3]	Conductivity [kg^-1m^-1s³K]	Heat Capacity [J kg^-1K^-1]
101	Neuchatel 1946-1960 adapted	Rendering	0.02	1800.0	0.87	1100.0
		Rubble masonry	0.2	1600.0	0.81	1045.0
		Air gap	0.06	1.2	0.033	1005.0
		Hollow clay brick	0.08	1600.0	0.8	900.0
		Plaster	0.01	1200.0	0.43	1000.0
102	Neuchatel 1961-1970 adapted	Rendering	0.02	1800.0	0.87	1100.0
		Hollow clay brick	0.15	1600.0	0.8	900.0
		Expanded polystyrene 10-50 kg/m3	0.03	30.0	0.06	1450.0
		Hollow clay brick	0.06	1600.0	0.8	900.0
		Plaster	0.01	1200.0	0.43	1000.0
103	Neuchatel 1971-1980 adapted	Rendering	0.02	1800.0	0.87	1100.0
		Hollow clay brick	0.15	1600.0	0.8	900.0
		Expanded polystyrene 10-50 kg/m3	0.04	30.0	0.06	1450.0
		Hollow clay brick	0.06	1600.0	0.8	900.0
		Plaster	0.01	1200.0	0.43	1000.0
104	Neuchatel 1981-1990 adapted	Rendering	0.02	1800.0	0.87	1100.0
		Expanded polystyrene 10-50 kg/m3	0.05	30.0	0.06	1450.0
		Reinforced concrete	0.17	2350.0	2.4	1000.0
		Plaster	0.01	1200.0	0.43	1000.0
105	Neuchatel 1991-2000 adapted	Rendering	0.02	1800.0	0.87	1100.0
		Expanded polystyrene 10-50 kg/m3	0.07	30.0	0.06	1450.0
		Reinforced concrete	0.17	2350.0	2.4	1000.0
		Plaster	0.01	1200.0	0.43	1000.0
106	Neuchatel 2001-2010 adapted	Rendering	0.02	1800.0	0.87	1100.0
		Expanded polystyrene 10-50 kg/m3	0.1	30.0	0.06	1450.0
		Reinforced concrete	0.17	2350.0	2.4	1000.0
		Plaster	0.01	1200.0	0.43	1000.0
107	CSD Lesosai 2011-2015	Rendering	0.02	1400.0	0.7	900.0
		Expanded polystyrene 10-50 kg/m3	0.12	30.0	0.046	1450.8
		Reinforced concrete	0.17	2350.0	2.4	1000.8
		Plaster	0.01	1800.0	0.99	849.6
108	CSD Lesosai 2015-2020	Rendering	0.02	1400.0	0.7	900.0
		Expanded polystyrene 10-50 kg/m3	0.16	30.0	0.036	1450.8
		Reinforced concrete	0.17	2350.0	2.4	1000.8
		Plaster	0.01	1800.0	0.99	849.6
111	Neuchatel 1946-1960 renovated	Expanded polystyrene	0.117	30.0	0.036	1450.0
		Rendering	0.02	1800.0	0.87	1100.0
		rubbleMasonry	0.2	1600.0	0.81	1045.0
		Air gap	0.06	1.2	0.033	1005.0
		Hollow clay brick	0.08	1600.0	0.8	900.0
		Plaster	0.01	1200.0	0.43	1000.0
112	Neuchatel 1961-1970 renovated	Expanded polystyrene	0.109	30.0	0.036	1450.0
		Rendering	0.02	1800.0	0.87	1100.0
		Hollow clay brick	0.15	1600.0	0.8	900.0
		Expanded polystyrene 10-50 kg/m3	0.03	30.0	0.06	1450.0
		Hollow clay brick	0.06	1600.0	0.8	900.0
		Plaster	0.01	1200.0	0.43	1000.0
113	Neuchatel 1971-1980 renovated	Expanded polystyrene	0.103	30.0	0.036	1450.0
		Rendering	0.02	1800.0	0.87	1100.0
		Hollow clay brick	0.15	1600.0	0.8	900.0
		Expanded polystyrene 10-50 kg/m3	0.04	30.0	0.06	1450.0
		Hollow clay brick	0.06	1600.0	0.8	900.0
		Plaster	0.01	1200.0	0.43	1000.0
114	Neuchatel 1981-1990 renovated	Expanded polystyrene	0.104	30.0	0.036	1450.0
		Rendering	0.02	1800.0	0.87	1100.0
		Expanded polystyrene 10-50 kg/m3	0.05	30.0	0.06	1450.0
		Reinforced concrete	0.17	2350.0	2.4	1000.0
		Plaster	0.01	1200.0	0.43	1000.0
115	Neuchatel 1991-2000 renovated	Expanded polystyrene	0.0978	30.0	0.036	1450.0
		Rendering	0.02	1800.0	0.87	1100.0
		Expanded polystyrene 10-50 kg/m3	0.07	30.0	0.06	1450.0
		Reinforced concrete	0.17	2350.0	2.4	1000.0
		Plaster	0.01	1200.0	0.43	1000.0
116	Neuchatel 2001-2010 renovated	Expanded polystyrene	0.0798	30.0	0.036	1450.0
		Rendering	0.02	1800.0	0.87	1100.0
		Expanded polystyrene 10-50 kg/m3	0.1	30.0	0.06	1450.0
		Reinforced concrete	0.17	2350.0	2.4	1000.0
		Plaster	0.01	1200.0	0.43	1000.0
117	CSD Lesosai 2011-2015 renovated	Expanded polystyrene	0.0461	30.0	0.036	1450.0
		Rendering	0.02	1400.0	0.7	900.0
		Expanded polystyrene 10-50 kg/m3	0.12	30.0	0.046	1450.8
		Reinforced concrete	0.17	2350.0	2.4	1000.8
		Plaster	0.01	1800.0	0.99	849.6
400	Ground Asphalt	Asphalt	0.025	2360.0	0.75	920.0
		Sand	0.02	1300.0	0.5	828.0
		Gravel	0.1	1800.0	0.7	792.0
		Moraine	0.85	1600.0	2.4	1200.0
401	Ground Green	Clay	0.025	1760.0	0.97	920.0
		Dense clay	0.02	1700.0	0.93	864.0
		Wet sand	0.1	1800.0	1.4	864.0
		Moraine	0.85	1600.0	2.4	1200.0
402	Water	Water	4.0	1000.0	0.57	4180.0
403	Clay soil	Soil	3.82	1600.0	0.25	890.0

Domestic Hot Water

Domestic hot water (DHW) demand profiles were derived from the output XML files generated by CitySim simulations. Each building includes occupant-related metadata specifying a daily DHW profile (DHWDayProfile) and an associated annual schedule (DHWYearProfile). The daily profiles provide normalized hourly distribution values for hot water use, while the annual profile assigns one such daily profile to each calendar day. These were combined to generate hourly DHW consumption time series for each building, scaled by the number of occupants and the per-person daily water consumption defined in the profile. The corresponding energy demand was computed by assuming cold water at 10 °C heated to 55 °C, using the specific heat capacity of water and converted to kWh. This yielded a physically consistent estimate of DHW-related energy use with hourly resolution over one year.

Domestic Hot Water

Heat pump sizing

The heat pump sizing methodology implemented in this study follows the procedure outlined in Appendices A and C of the Reference Framework for System Simulations of the IEA SHC Task 44 / HPP Annex 38 – Part B: Buildings and Space Heat Load. The sizing process begins with the construction of a synthetic yearly temperature profile by fitting a sinusoidal function to hourly outdoor temperature data. The 1st percentile of the residuals from this fit is used to determine a conservative design ambient temperature, ensuring that the sizing accounts for rare but critical cold weather events. Subsequently, a linear regression is performed between observed space heating demand and ambient temperature to estimate the building's heat load at the design temperature.

Using this design point (temperature and load), the procedure selects a heat pump from a predefined catalogue whose rated output at the given supply and return temperatures is closest to the estimated design heat load. If no available heat pump meets the full load, the required backup heating power is calculated. To reflect the variability in building envelope quality, the code dynamically assigns either low-temperature floor heating or high-temperature radiators depending on the simulated annual energy use intensity (kWh/m²) derived from CitySim outputs. This selection determines the design supply and return temperatures used in subsequent calculations. The selected sizing strategy thus balances robustness against extreme conditions with avoidance of oversizing, thereby supporting efficient system operation across the full range of expected temperatures. Supply and return temperatures for space heating and domestic hot water are then calculated based on the heat load and ambient conditions, allowing the estimation of seasonal COPs under variable operating conditions.

Building	Configuration	TSC [%]	DSC [%]	TSS [%]	Demand\_electric [kWh]	Demand\_thermal [kWh]	Generation [kWh]	Scenario
26930(Building-902138-CH895237183088)	HP	30.1	30.1	42.1	7969.2	15277.4	17367.9	2025 Roofs
26931(Building-902142-CH963752308786)	HP	42.1	42.1	36.7	5210.5	8691.6	6696.1	2025 Roofs
26932(Building-190183183-CH113098523737)	HP	24.4	24.4	44.6	9806.6	19598	28405.3	2025 Roofs
26933(Building-902139-CH503730975261)	HP	31.4	31.4	40.5	10725	21899.7	22162.5	2025 Roofs
26934(Building-902141-CH513082523745)	HP	32.5	32.5	41.3	7048.7	12283.3	13441.8	2025 Roofs
Aggregated	HP	29.8	29.8	41.5	40760	77750	88073.7	2025 Roofs
26930(Building-902138-CH895237183088)	HP	17.6	17.6	60	7969.2	12250.8	39477.9	RCP 85 2050 Roofs and Facade
26931(Building-902142-CH963752308786)	HP	20.4	20.4	62.4	5210.5	6985.6	22145	RCP 85 2050 Roofs and Facade
26932(Building-190183183-CH113098523737)	HP	16.6	16.6	57.4	9806.6	15817.5	49770.8	RCP 85 2050 Roofs and Facade
26933(Building-902139-CH503730975261)	HP	17.2	17.2	59.3	10725	17575.5	54758.4	RCP 85 2050 Roofs and Facade
26934(Building-902141-CH513082523745)	HP	19.2	19.2	58.5	7048.7	9894.3	30338.2	RCP 85 2050 Roofs and Facade
Aggregated	HP	17.8	17.8	59.2	40760	62523.7	196490.3	RCP 85 2050 Roofs and Facade
26930(Building-902138-CH895237183088)	HP	28.5	28.5	44.3	7969.2	12250.8	17919.3	RCP 85 2050 Roofs
26931(Building-902142-CH963752308786)	HP	40.9	40.9	39.1	5210.5	6985.6	6905.8	RCP 85 2050 Roofs
26932(Building-190183183-CH113098523737)	HP	23.1	23.1	46.8	9806.6	15817.5	29275.6	RCP 85 2050 Roofs
26933(Building-902139-CH503730975261)	HP	29.8	29.8	42.7	10725	17575.5	22850.5	RCP 85 2050 Roofs
26934(Building-902141-CH513082523745)	HP	31.1	31.1	43.3	7048.7	9894.3	13826.5	RCP 85 2050 Roofs
Aggregated	HP	28.4	28.4	43.7	40760	62523.7	90777.7	RCP 85 2050 Roofs
26930(Building-902138-CH895237183088)	HP	17.5	17.5	59.1	7969.2	13617.4	40402.1	RCP 26 2050 Roofs and Facade
26931(Building-902142-CH963752308786)	HP	20.2	20.2	61.3	5210.5	7766.3	22676.5	RCP 26 2050 Roofs and Facade
26932(Building-190183183-CH113098523737)	HP	16.5	16.5	56.1	9806.6	17592.3	50896.2	RCP 26 2050 Roofs and Facade
26933(Building-902139-CH503730975261)	HP	17.1	17.1	57.9	10725	19557.7	55980	RCP 26 2050 Roofs and Facade
26934(Building-902141-CH513082523745)	HP	19	19	57.4	7048.7	11008.9	30990.5	RCP 26 2050 Roofs and Facade
Aggregated	HP	17.7	17.7	58	40760	69542.7	200945.3	RCP 26 2050 Roofs and Facade
26930(Building-902138-CH895237183088)	HP	28.3	28.3	43.2	7969.2	13617.4	18239.3	RCP 26 2050 Roofs
26931(Building-902142-CH963752308786)	HP	40.8	40.8	38.5	5210.5	7766.3	7033.5	RCP 26 2050 Roofs
26932(Building-190183183-CH113098523737)	HP	22.8	22.8	45.4	9806.6	17592.3	29817.5	RCP 26 2050 Roofs
26933(Building-902139-CH503730975261)	HP	29.7	29.7	41.7	10725	19557.7	23272.1	RCP 26 2050 Roofs
26934(Building-902141-CH513082523745)	HP	30.8	30.8	42.3	7048.7	11008.9	14083.1	RCP 26 2050 Roofs
Aggregated	HP	28.2	28.2	42.6	40760	69542.7	92445.5	RCP 26 2050 Roofs

Building	Configuration	TSC [%]	DSC [%]	ISC [%]	TSS [%]	Demand\_electric [kWh]	Demand\_thermal [kWh]	Generation [kWh]	Scenario
26930(Building-902138-CH895237183088)	HP & BATT	35.6	20.8	14.8	47.2	7969.2	15277.4	17367.9	2025 Roofs
26931(Building-902142-CH963752308786)	HP & BATT	47.4	34	13.4	39.8	5210.5	8691.6	6696.1	2025 Roofs
26932(Building-190183183-CH113098523737)	HP & BATT	29.5	15.6	13.9	50.6	9806.6	19598	28405.3	2025 Roofs
26933(Building-902139-CH503730975261)	HP & BATT	37.4	21.3	16.1	45.5	10725	21899.7	22162.5	2025 Roofs
26934(Building-902141-CH513082523745)	HP & BATT	37.9	23.7	14.2	45.9	7048.7	12283.3	13441.8	2025 Roofs
Aggregated	HP & BATT	35.3	20.7	14.6	46.5	40760	77750	88073.7	2025 Roofs
26930(Building-902138-CH895237183088)	HP & BATT	20.5	10	10.5	65.4	7969.2	12250.8	39477.9	RCP 85 2050 Roofs and Facade
26931(Building-902142-CH963752308786)	HP & BATT	23.4	13.7	9.7	67.7	5210.5	6985.6	22145	RCP 85 2050 Roofs and Facade
26932(Building-190183183-CH113098523737)	HP & BATT	20	9.6	10.4	63.9	9806.6	15817.5	49770.8	RCP 85 2050 Roofs and Facade
26933(Building-902139-CH503730975261)	HP & BATT	20.5	10.1	10.4	65.6	10725	17575.5	54758.4	RCP 85 2050 Roofs and Facade
26934(Building-902141-CH513082523745)	HP & BATT	22.4	11.9	10.5	64.3	7048.7	9894.3	30338.2	RCP 85 2050 Roofs and Facade
Aggregated	HP & BATT	21	10.7	10.4	65.2	40760	62523.7	196490.3	RCP 85 2050 Roofs and Facade
26930(Building-902138-CH895237183088)	HP & BATT	33.8	20.3	13.6	49.8	7969.2	12250.8	17919.3	RCP 85 2050 Roofs
26931(Building-902142-CH963752308786)	HP & BATT	45.8	33.6	12.2	42.3	5210.5	6985.6	6905.8	RCP 85 2050 Roofs
26932(Building-190183183-CH113098523737)	HP & BATT	27.8	15.1	12.7	52.9	9806.6	15817.5	29275.6	RCP 85 2050 Roofs
26933(Building-902139-CH503730975261)	HP & BATT	35.5	20.7	14.9	48.2	10725	17575.5	22850.5	RCP 85 2050 Roofs
26934(Building-902141-CH513082523745)	HP & BATT	36.1	23.1	13	48	7048.7	9894.3	13826.5	RCP 85 2050 Roofs
Aggregated	HP & BATT	33.6	20.2	13.4	48.9	40760	62523.7	90777.7	RCP 85 2050 Roofs
26930(Building-902138-CH895237183088)	HP & BATT	21	10	11	65.6	7969.2	13617.4	40402.1	RCP 26 2050 Roofs and Facade
26931(Building-902142-CH963752308786)	HP & BATT	23.7	13.4	10.3	67.8	5210.5	7766.3	22676.5	RCP 26 2050 Roofs and Facade
26932(Building-190183183-CH113098523737)	HP & BATT	20.2	9.5	10.6	63.5	9806.6	17592.3	50896.2	RCP 26 2050 Roofs and Facade
26933(Building-902139-CH503730975261)	HP & BATT	20.7	10.2	10.5	65.2	10725	19557.7	55980	RCP 26 2050 Roofs and Facade
26934(Building-902141-CH513082523745)	HP & BATT	22.6	11.7	10.8	63.9	7048.7	11008.9	30990.5	RCP 26 2050 Roofs and Facade
Aggregated	HP & BATT	21.2	10.6	10.7	64.9	40760	69542.7	200945.3	RCP 26 2050 Roofs and Facade
26930(Building-902138-CH895237183088)	HP & BATT	33.3	19.9	13.5	48.2	7969.2	13617.4	18239.3	RCP 26 2050 Roofs
26931(Building-902142-CH963752308786)	HP & BATT	45.7	33.3	12.4	41.6	5210.5	7766.3	7033.5	RCP 26 2050 Roofs
26932(Building-190183183-CH113098523737)	HP & BATT	27.5	14.8	12.7	51.4	9806.6	17592.3	29817.5	RCP 26 2050 Roofs
26933(Building-902139-CH503730975261)	HP & BATT	35.2	20.4	14.8	46.8	10725	19557.7	23272.1	RCP 26 2050 Roofs
26934(Building-902141-CH513082523745)	HP & BATT	35.7	22.7	13	46.8	7048.7	11008.9	14083.1	RCP 26 2050 Roofs
Aggregated	HP & BATT	33.2	19.8	13.4	47.6	40760	69542.7	92445.5	RCP 26 2050 Roofs

Code repositories will be released after the presentation of the poster at CISBAT 2025.

openbeers-server
openbeers-client
openbeers-py
openbeers-js
openbeers-data
CitySim-Solver
BASOPRA

BibTeX


    @article{geissbuhler2025openbeers,
        title={OpenBEERS: A digital platform for urban scale simulation of building energy efficiency},
        author={David Geissbühler, Alejandro Pena Bello, Jérôme Kämpf, Jakob Rager},
        journal={To appear},
        year={2025}
    }