# MopoHeatSectorDataProcessing/output

Output produced by MopoHeatSectorDataProcessing, provided as a separate repository due to its size not really befitting a GitHub repository.

Most of the contents of this description are copied from `MopoHeatSectorDataProcessing/output/README.md`, so all the references might not make sense within the context of just these output files.

## cop-timeseries/*.csv

Hourly timeseries for different heat pump coefficients of performance for different countries, calculated using the Mopo AmBIENCE2ABM Demo. Note that the COP approximation uses a rather crude constant exergy Carnot method.

Contains 5 weather years:

• 1995
• 2008
• 2009
• 2012
• 2015

Contains 8 different COP timeseries:

• `A2AHP-air` space heating using an air-to-air heat pump (`A2AHP`).
• `A2AHP-cooling` space cooling using an air-to-air heat pump.
• `A2WHP-DHW` domestic hot water (`DHW`) heating using an air-to-water heat pump (`A2WHP`).
• `A2WHP-floor` space heating using an air-to-water heat pump assuming a low-temperature heat distribution system (e.g. hydronic floor heating).
• `A2WHP-radiators` space heating using an air-to-water heat pump assuming a medium-temperature heat distribution system (e.g. modern hydronic radiators).
• `G2WHP-DHW` domestic hot water heating using an ground-to-water heat pump (`G2WHP`).
• `G2WHP-floor` space heating using an ground-to-water heat pump assuming a low-temperature heat distribution system (e.g. hydronic floor heating).
• `G2WHP-radiators` space heating using an ground-to-water heat pump assuming a medium-temperature heat distribution system (e.g. modern hydronic radiators).

Produced by `cop_export.ipynb` with more details on how the data is reorganized from the raw ArchetypeBuildingModel output.

## `demand-timeseries/*.csv`

Normalised hourly timeseries for the useful energy demand for space `heating` and `cooling`, as well as domestic hot water (`DHW`) heating for different countries, calculated using the Mopo AmBIENCE2ABM Demo. The demands are also provided for residential (`res`) and nonresidential (`nonres`) buildings separately. Note that the timeseries are rather crude due to the underlying assumptions and shortcomings of ArchetypeBuildingModel.

The timeseries are normalised such that when scaled with a yearly demand in GWh, the resulting timeseries will represent hourly demand in MW. Note that in order to preserve the variations in heating/cooling demands between weather years, the timeseries are normalised using their average yearly demand, and thus do not sum to 1. This roughly corresponds to the approach used in the Hotmaps project when projecting future heating/cooling demand scenarios, where average weather for 2002-2012 was used.

## `heat_storage_params.csv`

## `heat_technology_params.csv`

## `peak_to_yearly_demand_ratios_MW_GWh.csv`

## `scenario_estimated_existing_capacities_MW.csv`

Estimated heating/cooling technology capacities in MW based on the Hotmaps project results as well as the peak-to-yearly-demand ratios of the ArchetypeBuildingModel. Essentially just multiplies the yearly demand by the estimated peak demand for distributed heating capacities. District heating heat-only boiler capacities estimated based on fuel distributions in the JRC-IDEES 2015 (for UK only) and 2021 datasets.

NOTE! The existing heating capacities of the heat pumps are pretty volatile, as the `Electricity` vs `ambient heat` ratios in the Hotmaps data is all over the place. For most cases this is hopefully ok, but for some rows the estimated COP reaches hundreds and thousands, which is unrealistic. Quirks of statistics, I suppose.

Produced by `process_capacity_data.ipynb` with more details on how the capacities are mapped and estimated.

## `scenario_total_yearly_demands_GWh.csv`

Total yearly space heating/cooling and domestic hot water heating demands based on the Hotmaps project results. These are used to scale the heating/cooling demand profiles to match a desired yearly scenario demand.

NOTE! While Norway and Switzerland are included, they don't have real scenario data in the Hotmaps results. Instead, their demands are based on neighbouring countries and scaled based on population.

Produced by `demand_scaling.ipynb` with more details on how the Hotmaps data is extended and aggregated.

## `simplified_capacities_with_decommissioning_MW.csv`

Further processed simplified existing capacities from `scenario_estimated_existing_capacities_MW` for Mopo WP5. Unlike the aforementioned file, the future capacities here are not based on the Hotmaps project results except for the first year. Instead, the capacities for the remaining years are estimated based on approximate decommissioning rates calculated from the `lifetime_y` parameter in the `heating_technology_params.csv`.

Also note that these capacities are totals over the `building_category` and heating/DHW `demand`. `demand_category` and cooling `demand` are still separate due to different `technology` serving them. The `scenario` is currently selected as `current`.

Produced by `process_capacity_data.ipynb`.