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ro-heat

The ro-heat tool is used to estimate the residential building heat demand on a building level. It uses statistical data and building characteristics to estimate the heat demand for each building in the dataset using a single-zone resistance–capacitance model (RC model).

Under Development

This documentation section is currently being expanded. The tool is fully functional, but detailed documentation is pending.

Maintainer: Martin

Data Sources Used

Key Features

  • Extraction of building components: Building components (external walls, roofs and ground floors) are extracted
  • Refurbishment simulation: For each component refurbishment is simulated stochastically by sampling refurbishment intervals from a normal distribution parametrised by component-specific mean lifespans and standard deviations
  • Assignment of building archetypes: Based on the refurbishment status, each building component is assigned a TABULA archetype that specifies the used materials and their thermal properties
  • Parameter estimation: Based on the used materials, we estimate the required parameters for the RC model, e.g., resistance and capacitance, per component and aggregate it per building
  • Demand estimation: Lastly, the parameters are input into EnTiSe with corresponding weather data and temperature set points to estimate heating and cooling demand

Output Data

The output datasets are stored in the ro_heat schema (the output schema specified in the configuration) of the infDB PostgreSQL database. The main tables created are:

  • buildings_refurbished_status: Contains detailed building information with attributes such as type, construction year, number of floors, component areas and component refurbishment status.
  • buildings_rc: Contains parameters for the RC model per building.

  • entise_summary: Contains heating and cooling demand and load summaries per building.

Configuration

The configuration of the tool can be done via the configuration YAML file:

configs/config-ro-heat.yml
ro-heat:
    config-infdb: "config-infdb.yml" # only filename - change path in ".env" file "CONFIG_INFDB_PATH"
    logging:
        path: "ro-heat.log"
        level: "INFO" # ERROR, WARNING, INFO, DEBUG
    hosts:
        postgres:
            user: None
            password: None
            db: None
            host: None
            exposed_port: None
            epsg: None
    data:
        input:
            schema: basedata # (1)
            simulation_year: 2023 # (2)
            heating_setpoint: 20.0 # (3)
            random_seed: 42 # (4) 
            method: 1R1C # (5)
        refurbishment:
            outer_wall: # (6)
                quota: 0.33 # (7)
                lifespan_mean: 40 # (8)
                lifespan_spread: 10 # (9)
            rooftop:
                quota: 0.63
                lifespan_mean: 50
                lifespan_spread: 10
            window:
                quota: 0.9
                lifespan_mean: 30
                lifespan_spread: 10
        output:
            schema: ro_heat # (10)

  1. Specify the schema where the input data comes from.
  2. Specify the simulation year, ensure weather data is available for this year.
  3. Specify the heating setpoint in °C, this is input in EnTiSe as min_temperature[C] and init_temperature[C].
  4. Specify the random seed for the refurbishment simulation for reproducibility.
  5. Specify the demand estimation, this is input in EnTiSe as method.
  6. Specify refurbishment parameters per component, e.g., for outer walls.
  7. Specify refurbishment quota for outer walls, e.g., 0.33 means that a third of outer walls will be refurbished at the end of their lifespan.
  8. Specify the mean lifespan for outer walls in years. Refurbishment intervals are sampled from a normal distribution parametrised by mean lifespan and spread.
  9. Specify the spread of the lifespan for outer walls in years.
  10. Specify the schema where the data should be stored.