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
Worked on the Multi-Energy-Systems-Optimization/mesido repository to enhance time-series energy modeling by enabling separate discretization of peak days for heating and cooling demands. Updated the adapt_profiles.py script using Python to support distinct peak-day handling, addressing scenarios where peak periods for heating and cooling do not align. Incorporated comprehensive unit tests to validate the new logic, particularly for overlapping or sequential peak days, ensuring robust and reliable behavior. Leveraged data analysis and time series analysis skills, working with CSV and XML data formats. This feature improved the accuracy of energy demand forecasts, supporting more informed optimization and planning decisions for energy systems.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
872
Activity Months1
Your Network
19 peopleSame Organization
@tno.nl14
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
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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
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