Skip to main content

SHRECC

Simple Hourly Resolution Electricity Consumption Calculation

Description

SHRECC creates time-resolved electricity-consumption databases for Brightway. It uses measured Energy Charts data for historical years and ENTSO-E TYNDP scenario data for prospective years, while exposing the same workflow for both.

Features

  • Hourly production, trade, consumption-volume, and consumption-mix results.
  • One reduced country-network solver shared by Energy Charts and TYNDP data.
  • Historical ecoinvent allocation and prospective premise/IAM mapping.
  • Annual or monthly Brightway inventories from range-based or explicit timestamp selections, plus hourly LCIA without hourly foreground activities.
  • Inspectable intermediate results before any Brightway database is changed.
  • Resumable source acquisition and compressed, time-chunked result caches.
  • Brightway 2 and 2.5 database writing through NewDatabase.
  • Multi-year runs with a separate, year-labelled foreground database for each requested year.

Documentation

The full documentation is hosted at Read the Docs page for shrecc

The internal data flow and module responsibilities are summarized in docs/architecture.md.

Installation

shrecc can be installed from pypi or from source.

From pypi

The package is published at pypi.org/projects/shrecc. You can install it with pip (or any other pypi compatible util like uv or poetry as follows:

pip install shrecc

Install optional premise geography support or the notebook environment with:

pip install "shrecc[premise,notebooks]"

From source

To install shrecc from source, clone the code and then install the package and if necessary the dependencies manually.

Usage

The repository contains a minimal getting-started notebook and a more detailed analysis notebook. Both are also available in the documentation (only for main releases) at read the docs.

The harmonized historical/prospective workflow is available through NewDatabase:

from shrecc import NewDatabase

electricity = NewDatabase(
    scenario="DE",
    years=[2035, 2040, 2050],
    climate_year=2009,
    bg_db_name={
        2035: "premise-remind-eu-2035",
        2040: "premise-remind-eu-2040",
        2050: "premise-remind-eu-2050",
    },
    my_db_name="shrecc_tyndp_DE_june_noon",
    countries=["ES", "FR", "DE", "IT", "PT", "BE", "NL", "LU", "AT", "CH"],
    time_range=["2040-06-01 00:00:00", "2040-06-30 23:00:00"],
    hour_range=[10, 14],
    project_name="SHRECCei311",
    source="auto",
    inventory_resolution="annual",
)

electricity.create()
electricity.write()

For a multi-year run, the month/day/time selection is reused for each year and write() creates one foreground database per year. A year suffix is added to my_db_name automatically unless explicit year-specific names are supplied. Foreground activity names and metadata also record their modeled period.

inventory_resolution controls how many foreground activities are written: "annual" creates one activity per country and year, "monthly" creates one per country and selected calendar month. Both resolutions apply consumption_profile within each period. Hourly inventories are not written as Brightway activities.

Hourly LCIA is calculated directly from the retained hourly mixes. By default, SHRECC selects every installed EF v3.1 impact category, scores each unique background input once, and combines those scores with the hourly coefficients:

lcia_results = electricity.lcia()
hourly_intensity = lcia_results.hourly(2040)["intensity"]
annual_intensity = lcia_results.annual()

Use electricity.lcia(methods=..., engine="multilca") to validate the optimized calculation with composite Brightway MultiLCA functional units. Neither engine writes hourly foreground activities.

Intermediate canonical results and annual/monthly mapped tables remain available on the class instance through results(year) and table(year). Use mapping_report(year) to inspect any historical technologies assigned to a country-specific high-voltage fallback activity.

Contributing

Please take a look at the DEVELOPPING.md file for details on how to contribute code to the repository. Notably for the versioning.

License

Copyright © 2025 Luxembourg Institute of Science and Technology Licensed under the MIT License.

Authors

Contributors

Metadata

Release files for shrecc 0.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for shrecc 0.2.0
File Size Uploaded
shrecc-0.2.0.tar.gz 214.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for shrecc 0.2.0
File Interpreter ABI Platform
shrecc-0.2.0-py3-none-any.whl Python 3 none any Details

Total release size: 475.7 kB

Release files / shrecc-0.2.0.tar.gz

Download URL shrecc-0.2.0.tar.gz
Size 214.2 kB
Tags Source
SHA-256 checksum
How to use checksums
d2fbf7101d59afe3b9b632ba8a842d93240e8b0e070a215029e846e7a9575afd
BLAKE2b-256 checksum
How to use checksums
5c868956caaa70c941697d92a277e583e6c240199cb476d793558751339c9d8e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.12.14

Release files / shrecc-0.2.0-py3-none-any.whl

Download URL shrecc-0.2.0-py3-none-any.whl
Size 261.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
3982bd12ed51ec96d0c7fc7937554953e6b43fd48177bb6ffbc8b43bcd653d10
BLAKE2b-256 checksum
How to use checksums
2112e9e8d048d95286796a65c45746dddcaa0ec07bf13562597485b3be87d5d5
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.12.14
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page