add release notes; rename artificial to synthetic

config/config.default.yaml

@@ -292,7 +292,7 @@ load:
   manual_adjustments: true # false
   scaling_factor: 1.0
   fixed_year: false # false or year (e.g. 2013)
-  supplement_missing_data_artificially: true
+  supplement_synthetic: true
 
 # docs
 # TODO: PyPSA-Eur merge issue in prepare_sector_network.py

doc/configtables/load.csv

@@ -4,4 +4,4 @@ time_shift_for_large_gaps,string,string,"Periods which are used for copying time
 manual_adjustments,bool,"{true, false}","Whether to adjust the load data manually according to the function in :func:`manual_adjustment`."
 scaling_factor,--,float,"Global correction factor for the load time series."
 fixed_year,--,Year or False,"To specify a fixed year for the load time series that deviates from the snapshots' year"
-supplement_missing_data_artificially,bool,"{true, false}","Whether to supplement missing data for selected time period should be supplemented by artificial data from https://zenodo.org/record/7070438/files/demand_hourly.csv."
+supplement_synthetic,bool,"{true, false}","Whether to supplement missing data for selected time period should be supplemented by synthetic data from https://zenodo.org/record/10820928."

doc/release_notes.rst

@@ -10,6 +10,52 @@ Release Notes
 Upcoming Release
 ================
 
+* Enhanced support for choosing different weather years
+  (https://github.com/PyPSA/pypsa-eur/pull/204):
+
+  - Processed energy statistics from eurostat (1990-2021) and IDEES (2000-2015)
+    are now initially stored for all available years and filtered by the year
+    given in ``energy: energy_totals_year:``.
+
+  - Added option to supplement electricity load data with synthetic time series
+    for years not contained in OPSD (from https://zenodo.org/records/10820928,
+    ``load: supplement_synthetic:``).
+
+  - The total annual heat demand for years not contained in the energy
+    statistics by eurostat (1990-2021) or IDEES (2000-2015)  are scaled based on
+    a regression between the total number of heating degree days and the total
+    annual heat demand between the years 2007-2021, assuming a similar building
+    stock.
+
+  - Added option to scale annual hydro-electricity generation data for years not
+    contained in the in EIA (1980-2021) based on a regression between annual
+    generation and total runoff per country for the years 1980-2021
+    (``renewable: hydro: eia_approximate_missing:``)
+
+  - Added option to normalize annual hydro generation data by the associated
+    installed capacity reported by EIA (1980-2021) in order to eliminate changes
+    in generation due to newly built capacity (``renewable: hydro:
+    eia_approximate_missing: eia_correct_by_capacity:``).
+
+  - Added option to make hydro generation data independent of weather year
+    (``renewable: hydro: eia_approximate_missing: eia_norm_year:``).
+
+  - Added option to drop leap days (``enable: drop_leap_day:``).
+
+  - Added option to make electric load data independent of weather year
+    (``load: fixed_year:``).
+
+  - Include time series of Swiss number of passenger vehicles from the `Swiss
+    Federal Statistical Office
+    <https://www.bfs.admin.ch/bfs/en/home/statistics/mobility-transport/transport-infrastructure-vehicles/vehicles/road-vehicles-stock-level-motorisation.html>`_.
+
+  - Updated hydro-electricity generation and capacity data from EIA.
+
+  - The easiest way to sweep over multiple weather years is to use the new
+    scenario management. An example for the necessary `create_scenarios.py`
+    script can be found in this `Github gist
+    <https://gist.github.com/fneum/47b857862dd9148a22eca5a2e85caa9a>`_.
+
 * Upgrade to Snakemake v8.5+. This version is the new minimum version required.
   To upgrade an existing environment, run ``conda install -c bioconda
   snakemake-minimal">=8.5"`` and ``pip install snakemake-storage-plugin-http``

doc/sector.rst

@@ -56,6 +56,11 @@ Rule ``build_energy_totals``
 
 .. automodule:: build_energy_totals
 
+Rule ``build_heat_totals``
+==============================================================================
+
+.. automodule:: build_heat_totals
+
 Rule ``build_gas_input_locations``
 ==============================================================================
 

rules/build_electricity.smk

@@ -26,9 +26,9 @@ rule build_electricity_demand:
         load=config_provider("load"),
     input:
         reported=ancient("data/electricity_demand_raw.csv"),
-        artificial=lambda w: (
-            ancient("data/load_artificial_raw.csv")
-            if config_provider("load", "supplement_missing_data_artificially")(w)
+        synthetic=lambda w: (
+            ancient("data/load_synthetic_raw.csv")
+            if config_provider("load", "supplement_synthetic")(w)
             else []
         ),
     output:

rules/retrieve.smk

@@ -196,15 +196,15 @@ if config["enable"]["retrieve"]:
 
 if config["enable"]["retrieve"]:
 
-    rule retrieve_artificial_load_data:
+    rule retrieve_synthetic_electricity_demand:
         input:
             storage(
                 "https://zenodo.org/records/10820928/files/demand_hourly.csv",
             ),
         output:
-            "data/load_artificial_raw.csv",
+            "data/load_synthetic_raw.csv",
         log:
-            "logs/retrieve_artificial_load_data.log",
+            "logs/retrieve_synthetic_electricity_demand.log",
         resources:
             mem_mb=5000,
         retries: 2

scripts/build_electricity_demand.py

@@ -307,12 +307,12 @@ if __name__ == "__main__":
     )
     load = load.apply(fill_large_gaps, shift=time_shift)
 
-    if snakemake.params.load["supplement_missing_data_artificially"]:
-        logger.info("Supplement missing data with artificial data.")
-        fn = snakemake.input.artificial
-        artificial_load = pd.read_csv(fn, index_col=0, parse_dates=True)
-        artificial_load = artificial_load.loc[snapshots, countries]
-        load = load.combine_first(artificial_load)
+    if snakemake.params.load["supplement_synthetic"]:
+        logger.info("Supplement missing data with synthetic data.")
+        fn = snakemake.input.synthetic
+        synthetic_load = pd.read_csv(fn, index_col=0, parse_dates=True)
+        synthetic_load = synthetic_load.loc[snapshots, countries]
+        load = load.combine_first(synthetic_load)
 
     assert not load.isna().any().any(), (
         "Load data contains nans. Adjust the parameters "