pik-piam/mrremind
Nov 2024 – Mar 2025
3 Months active
Languages Used
CSVRMarkdownShell
Technical Skills
Data AnalysisData ProcessingEnergy DataR ProgrammingData PreprocessingModel Input Preparation
robinhasse
PROFILE
Robinhasse
Robin Krekeler contributed to the pik-piam/mrremind repository by developing and refining energy data processing features over a three-month period. He implemented global disaggregation for IEA_ETP data, enhancing analytics capabilities and supporting downstream dashboards. Robin improved data handling and region mapping, updated model input preparation for China’s electricity demand, and introduced ONONSPEC-based energy demand projections using R and statistical modeling. His work included spline-based end-of-data estimation and CHN-specific data refinements, resulting in more accurate scenario planning and faster model convergence. Throughout, Robin applied data analysis, preprocessing, and package management skills, delivering robust, maintainable solutions for energy modeling workflows.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
4
Lines of code
1,042
Activity Months3
Your Network
59 people
Work History
March 2025
3 Commits • 3 Features
Mar 1, 2025March 2025 monthly summary for pik-piam/mrremind focused on delivering ONONSPEC-based energy demand projection improvements, spline-based end-of-data estimation, and CHN-specific data refinement with a package update. These changes collectively improve projection accuracy, data quality, and model convergence, enabling more reliable scenario planning and policy analysis.
3 Commits • 3 Features
Mar 1, 2025March 2025 monthly summary for pik-piam/mrremind focused on delivering ONONSPEC-based energy demand projection improvements, spline-based end-of-data estimation, and CHN-specific data refinement with a package update. These changes collectively improve projection accuracy, data quality, and model convergence, enabling more reliable scenario planning and policy analysis.
March 2025
February 2025
1 Commits
Feb 1, 2025February 2025 (Month: 2025-02) — pik-piam/mrremind focused on strengthening data integrity for the REMIND model by fixing input data for China buildings electricity demand and updating release metadata to improve reproducibility and traceability. The changes deliver near-term accuracy improvements and clearer release documentation, aligning model inputs with planned scenarios and reducing risk of mis-specified demand projections.
February 2025
1 Commits
Feb 1, 2025February 2025 (Month: 2025-02) — pik-piam/mrremind focused on strengthening data integrity for the REMIND model by fixing input data for China buildings electricity demand and updating release metadata to improve reproducibility and traceability. The changes deliver near-term accuracy improvements and clearer release documentation, aligning model inputs with planned scenarios and reducing risk of mis-specified demand projections.
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Key accomplishments and business value:\n- Implemented Global Disaggregation for IEA_ETP Data in pik-piam/mrremind, enabling global-level analytics and richer insights.\n- Reworked data handling, region mapping, and conversion functions to support disaggregated outputs; prepared for downstream analytics and dashboards.\n- Version bump released to reflect data schema and API updates, ensuring compatibility for consumers and CI/CD traces.
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Key accomplishments and business value:\n- Implemented Global Disaggregation for IEA_ETP Data in pik-piam/mrremind, enabling global-level analytics and richer insights.\n- Reworked data handling, region mapping, and conversion functions to support disaggregated outputs; prepared for downstream analytics and dashboards.\n- Version bump released to reflect data schema and API updates, ensuring compatibility for consumers and CI/CD traces.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture76.0%
Performance64.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVMarkdownRShell
Technical Skills
Data AnalysisData PreprocessingData ProcessingEnergy DataModel Input PreparationPackage ManagementR ProgrammingStatistical ModelingTime Series Forecasting
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