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
Over a three-month period, contributed to the pik-piam/mrremind repository by developing and refining energy data processing features using R and Shell scripting. Delivered global disaggregation for IEA_ETP data, enabling enhanced analytics and supporting downstream dashboards. Improved model input preparation by implementing ONONSPEC-based energy demand projections, introducing spline-based end-of-data estimation, and refining China-specific data handling for more accurate scenario planning. Addressed data integrity by fixing input data for China buildings electricity demand and updating release metadata for reproducibility. The work demonstrated depth in data analysis, statistical modeling, and package management, resulting in more reliable and efficient 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
60 peopleShared Repositories
24
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
Repositories Contributed To
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