Skip to main content

An extension to the agent-based electricity market model AMIRIS providing external electricity price forecasts to the PriceForecasterApi agent

Project description

AMIRIS-PriceForecast

External electricity price forecasts to AMIRIS

PyPI version License Zenodo Code style: black

AMIRIS-PriceForecast is an extension to the agent-based electricity market model AMIRIS. Specifically, it provides electricity price forecasts to the PriceForecasterApi agent.

amiris_ml_price_forecasting.png

What is AMIRIS-PriceForecast?

AMIRIS-PriceForecast is a Python package designed to be used with AMIRIS. Specifically, it provides several time series forecasting algorithms that can be accessed via the UrlModelService. To do this, AMIRIS-PriceForecast sets up a server and loads a user-defined forecast model. It then waits for a ForecastApiRequest sent by the PriceForecasterApi agent. After providing the forecast, which may include probabilistic forecasts, it returns a ForecastApiResponse to the AMIRIS agent, where the simulation is resumed.

Who is AMIRIS-PriceForecast for?

This AMIRIS extension is suitable for energy system modellers who want to extend the capabilities of the MarketForecaster in AMIRIS. The forecasting algorithms of AMIRIS-PriceForecast can vary from simple time-shifting approaches (Hyndman, 2014) to state-of-the-art algorithms such as Transformers (Lim et al., 2021). However, this feature is aimed at more experienced users of AMIRIS, as it requires knowledge of the implications and limitations of forecasting in AMIRIS, as well as an understanding of the capabilities of time series forecasting techniques. We are happy to assist you in this regard, please refer to our Support Page for more details.

How to use AMIRIS-PriceForecast?

See the detailed description in the Wiki on setup, usage, and available forecast models of AMIRIS-PriceForecast.

Community

As for the main AMIRIS repository, AMIRIS-PriceForecast is mainly developed by the German Aerospace Center, Institute of Networked Energy Systems. We provide multi-level support for AMIRIS users: please see our dedicated Support Page. We welcome all contributions: bug reports, feature requests, and, of course, code. Please see our Contribution Guidelines.

Citing AMIRIS-PriceForecast

If you use AMIRIS-PriceForecast in an academic context please cite doi: 10.5281/zenodo.14907870 and doi: 10.21105/joss.05041. In other contexts, please include a link to our repositories AMIRIS-PriceForecast and AMIRIS.

Acknowledgements

The development of AMIRIS-PriceForecast was funded by the German Federal Ministry of Education and Research in the project FEAT (01IS22073B).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

amiris_priceforecast-1.0.0.tar.gz (19.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

amiris_priceforecast-1.0.0-py3-none-any.whl (26.0 kB view details)

Uploaded Python 3

File details

Details for the file amiris_priceforecast-1.0.0.tar.gz.

File metadata

  • Download URL: amiris_priceforecast-1.0.0.tar.gz
  • Upload date:
  • Size: 19.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.0 CPython/3.9.18 Windows/10

File hashes

Hashes for amiris_priceforecast-1.0.0.tar.gz
Algorithm Hash digest
SHA256 c4fbc4a381a08bc2780acfdf0cf9ec6810d953904d1ea2218f281746a8444e67
MD5 b9d5f0915bf180c76cf4126eeeb08fc5
BLAKE2b-256 c01b138f020fb960e92a28ad42e33c467d73f214d42d12921a46ed0457966320

See more details on using hashes here.

File details

Details for the file amiris_priceforecast-1.0.0-py3-none-any.whl.

File metadata

File hashes

Hashes for amiris_priceforecast-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 c490d0d7a7a8ce938db8c816a4448fcf8e95f9c8a4cc767cb337291de1a79487
MD5 8ca8fab691b7266cc7540ee5d3abf1ae
BLAKE2b-256 631c43b7ecca17e40e2ac28a8ad135fff0a2ef89f19241bc8ca39daed91ad58e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page