Multi-Energy-Systems-Optimization/mesido
Nov 2024 – Nov 2024
1 Month active
Languages Used
CSVPythonXML
Technical Skills
Data AnalysisEnergy Systems ModelingPython ScriptingTime Series AnalysisUnit Testing
Jaimepatt
PROFILE
Jaimepatt
Jaime Santiago Patterson enhanced the Multi-Energy-Systems-Optimization/mesido repository by developing a feature that enables separate discretization of peak days for heating and cooling within time-series energy modeling. Using Python and leveraging data analysis and unit testing skills, Jaime updated adapt_profiles.py to support distinct peak-day handling, addressing the challenge of differing peak periods for heating and cooling demands. The implementation included comprehensive tests to ensure correct behavior for both overlapping and sequential peak scenarios. This work improved the accuracy of energy demand forecasting, providing a more robust foundation for optimization and planning in energy systems modeling, and demonstrated disciplined engineering practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
872
Activity Months1
Your Network
17 people
Work History
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 performance focused on enhancing time-series energy modeling accuracy for the Multi-Energy-Systems-Optimization/mesido project. Delivered a feature to separately discretize peak days for heating and cooling, improving demand forecasting where peak periods differ by application. Updated adapt_profiles.py to support distinct peak-day handling and added tests to validate behavior for overlapping or sequential heating/cooling peaks. This work demonstrates strong Python/time-series engineering, test coverage, and disciplined version control. Major impact: more accurate energy demand forecasts, enabling better planning and optimization decisions. Commit reference: f93f939a97a440775aacc368f2d24da7f71931bd (166 adapt profiles for heating and cooling peak day seperately).
1 Commits • 1 Features
Nov 1, 2024November 2024 performance focused on enhancing time-series energy modeling accuracy for the Multi-Energy-Systems-Optimization/mesido project. Delivered a feature to separately discretize peak days for heating and cooling, improving demand forecasting where peak periods differ by application. Updated adapt_profiles.py to support distinct peak-day handling and added tests to validate behavior for overlapping or sequential heating/cooling peaks. This work demonstrates strong Python/time-series engineering, test coverage, and disciplined version control. Major impact: more accurate energy demand forecasts, enabling better planning and optimization decisions. Commit reference: f93f939a97a440775aacc368f2d24da7f71931bd (166 adapt profiles for heating and cooling peak day seperately).
November 2024
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVPythonXML
Technical Skills
Data AnalysisEnergy Systems ModelingPython ScriptingTime Series AnalysisUnit Testing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline