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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

[0.0.6] - 2026-05-18

Added

  • DWDClient for fetching weather data directly from the DWD Open Data API, replacing the wetterdienst dependency.
  • DWDClient multi-station merging, quality ranking, completeness report, and for_pvlib adapter for seamless pvlib integration.
  • HeatingRod component for electrical resistance heating simulation.
  • UserProfile.get_location_calibrated_consumerfactor() classmethod: fetches a full reference year of DWD daily observation data for a given lat/lon and computes the SigLinDe consumerfactor from station-specific temperatures, replacing the generic 2015 CSV calibration. Result is cached for 24 h by DWDClient.
  • consumerfactor parameter on UserProfile.__init__ to inject a pre-computed value and skip the automatic per-instance recalculation.
  • Operator.assign_assets_to_buses and Operator.build_timeseries_net methods.
  • Environment.get_observations now forwards resolution and additional DWD kwargs.
  • Proper pytest test suite under tests/ with integration tests that fetch live DWD data (@pytest.mark.integration).
  • DWDClient exported from vpplib.__init__.
  • Old ad-hoc test scripts preserved as usage examples in examples/.

Changed

  • Switched tqdm imports to tqdm.auto for correct rendering in both terminals and notebooks; added descriptive desc= labels to all progress bars.
  • Unified timestamp handling across all components: value_for_timestamp() and observations_for_timestamp() now accept int, str, datetime.datetime, and pd.Timestamp via isinstance() dispatch. Removed reliance on the private pd._libs.tslibs.timestamps.Timestamp type.
  • Replaced all type(x) == Y comparisons with isinstance() throughout the codebase.
  • Converted informal Info / ---- docstring headers to NumPy-style across all component modules.
  • Removed legacy # -*- coding: utf-8 -*- file headers from all modules.
  • Translated remaining German comments and TODOs to English.
  • Alphabetically sorted imports in __init__.py.
  • All example demos migrated from 2015 CSV files to live DWD observation data.
  • demo_combined_heat_and_power.py MOSMIX section uses UserProfile.get_location_calibrated_consumerfactor() for station-specific demand scaling instead of the generic 2015-CSV-derived value.

Removed

  • wetterdienst and marshmallow dependencies (replaced by DWDClient using requests, lxml, and pytz).

Fixed

  • tqdm progress bars no longer produce multi-line output in Jupyter notebooks.
  • operator.py no longer casts timestamps to str before passing to value_for_timestamp().
  • TypeError: Cannot compare tz-naive and tz-aware timestamps when running CombinedHeatAndPower, HeatPump, or HeatingRod with a MOSMIX environment (use_timezone_aware_time_index=True): last_ramp_up / last_ramp_down sentinels now initialize from timeseries.index[0] instead of thermal_energy_demand.index[0].
  • KeyError in ThermalEnergyStorage.operate_storage when indexing with tz-aware MOSMIX data: all three thermal generator components now strip timezone info from the demand index at construction time.
  • UserProfile.get_consumerfactor() inflating demand ~100× for short MOSMIX forecast windows: recalculation is now skipped when self.consumerfactor is already set.
  • MOSMIX simulation loop in demo_combined_heat_and_power.py raising KeyError near the end of the horizon: loop now iterates over thermal_energy_demand.index.
  • Tz-naive/tz-aware mismatch in HeatPump MOSMIX COP calculation.
  • Environment quality DataFrame initialized with float dtype to silence pandas FutureWarning.
  • PySAMBatteryStateful.prepare_time_series and Hydrogen.prepare_time_series now iterate over residual_load.index to avoid index errors.

[0.0.5] - 2025-05-16

Changed

  • Lost in progress ;)

[0.0.4] - 2025-03-17

Changed

  • Optimized requirements.txt and setup.py dependencies
  • Removed unnecessary dependencies that were causing installation issues
  • Added specific version requirements for wetterdienst (0.89.0) and marshmallow (3.20.1) to ensure compatibility
  • Added missing dependencies: pandas, numpy, and polars

Fixed

  • Installation failures on non-Windows platforms by removing platform-specific dependencies
  • Compatibility issues with newer versions of wetterdienst

[0.0.3] - Previous release

Initial public release.