Preprocessing

Preprocessing functions for NY grid data.

Created: 2023-12-26, by Bo Yuan (Cornell University) Last modified: 2023-12-26, by Bo Yuan (Cornell University)

nygrid.preprocessing.add_load_weighted(hourly_load_zonal: DataFrame, bus_info: DataFrame) → DataFrame[source]

Distribute zonal load to individual buses based on load distribution ratio.

Parameters:

  • hourly_load_zonal (pd.DataFrame) – Zonal load timeseries

  • bus_info (pd.DataFrame) – Bus information

Returns:

bus_wload – Bus-level load timeseries

Return type:

pd.DataFrame

nygrid.preprocessing.agg_demand_county2bus(demand_inc_county: DataFrame, county2bus: DataFrame) → DataFrame[source]

County-level consumption to bus-level consumption.

Parameters:

  • demand_inc_county (pd.DataFrame) – County-level consumption

  • county2bus (pd.DataFrame) – County to bus mapping

Returns:

demand_inc_bus – Bus-level consumption

Return type:

pd.DataFrame

nygrid.preprocessing.get_building_load_change_county(county_id: str, upgrade_id: int, bldg_type_list: List[str], bldg_proc_dir: str) → Tuple[DataFrame, DataFrame, DataFrame][source]

Read building timeseries data aggregated by county and building type.

Parameters:

  • county_id (str) – County ID

  • upgrade_id (int) – Upgrade ID

  • bldg_type_list (list) – List of building types

  • bldg_proc_dir (str) – Directory for processed building data

Returns:

  • df_county_base (pd.DataFrame) – Dataframe with baseline energy consumption

  • df_county_future (pd.DataFrame) – Dataframe with future energy consumption

  • df_county_saving (pd.DataFrame) – Dataframe with energy savings