pyam: Analysis and visualisation of integrated assessment and macro-energy scenarios

Abstract

The open-source Python package pyam provides a suite of features and methods for the analysis, validation and visualization of reference data and scenario results generated by integrated assessment models, macro-energy tools and other frameworks in the domain of energy transition, climate change mitigation and sustainable development. It bridges the gap between scenario processing and visualisation solutions that are "hard-wired" to specific modelling frameworks and generic data analysis or plotting packages. The package aims to facilitate reproducibility and reliability of scenario processing, validation and analysis by providing well-tested and documented methods for timeseries aggregation, downscaling and unit conversion. It supports various data formats, including sub-annual resolution using continuous time representation and "representative timeslices". The code base is implemented following best practices of collaborative scientific-software development. This manuscript describes the design principles of the package and the types of data which can be handled. The usefulness of pyam is illustrated by highlighting several recent applications.

Comparing energy system optimization models and integrated assessment models: Relevance for energy policy advice

“…In parallel to identifying harmonised regions, we embarked on an investigation of common variables across both ESOMs and IAMs. The reporting standard in the ECEMF project follows the IAMC-format, defined in the community-wide used database managed by IIASA and extensively used in many model intercomparison projects and in IPCC AR6 ( Huppmann et al , 2021 ). In total, there are over 1,000 variables defined in the IAMC template, but only a subset of these are relevant to this study.…”

Disclosing the heat density of district heating in Austria in 2050 under the remaining European CO2 budget of the 1.5 °C climate target

“…It is solved with the solver Gurobi version 9.0.3. For data analysis, we use the IAMC (Integrated Assessment Modeling Consortium) common data format template with the open-source Python package pyam [56]. All materials used in this work are available in the author's GitHub webpage.…”

Open energy system modelling to support the European Green Deal

“…It presents new open datasets ( Sterl et al , 2022 ) and new methods for citizen-driven data collection ( del Cañizo et al , 2021 ) as well as a better processing of open data sets ( Fleischer, 2022 ) to increase the use of open data in modelling. Additionally, the collection presents a new tool for the analysis and visualisation of results ( Huppmann et al , 2022 ) to streamline the postprocessing pipeline.…”

“…The processing and visualisation of results remains a time-consuming hurdle. With pyam , Huppmann et al (2022) hope to solve this issue by bridging the gap between different data formats and modelling frameworks to provide a way for all energy system and integrated assessment modellers to easily process and visualise their data. pyam is a Python toolbox with clear design principles, based on lessons learned from several Horizon 2020 projects and the Integrated Assessment Modeling Consortium (IAMC) data format.…”