OpenSTEF/openstef
Dec 2024 – Dec 2024
1 Month active
Languages Used
CSVPython
Technical Skills
Data AnalysisData EngineeringFeature EngineeringNumPyPandasPython
Majid Khoshrou
PROFILE
Majid Khoshrou
Majid Khoshrou enhanced the OpenSTEF/openstef repository by developing two new time-series features aimed at improving model input quality and capturing seasonal patterns. He engineered cyclic time features using sine and cosine transforms for time of day, day of week, and month, implemented as modular Python components with updated tests and data. Additionally, he integrated hourly daylight terrestrial radiation data from CSV files, building ingestion and merge functions that handle timezone-aware processing and ensure data integrity. Leveraging Python, Pandas, and feature engineering techniques, Majid’s work expanded the feature set and improved the consistency and robustness of the project’s ETL pipeline.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
26,904
Activity Months1
Your Network
32 people
Work History
December 2024
2 Commits • 2 Features
Dec 1, 2024December 2024 performance summary for OpenSTEF/openstef: Delivered two time-series feature enhancements to improve model inputs and capture seasonal patterns. Implemented cyclic time features using sine/cosine transforms for time components (time of day, day of week, month) with new feature generation modules, and updated tests/data to reflect the new features. Added daylight terrestrial radiation feature by integrating an hourly radiation CSV (May 2023 to May 2024) with an ingestion/merge function, and refactoring tests/data to accommodate timezones and data integrity. No major bugs fixed this month. Overall impact: expanded feature set, potential uplift in forecasting accuracy and robustness, improved data quality and ETL consistency. Technologies/skills demonstrated: Python, time-series feature engineering, modular design, data integration, CSV handling, timezone-aware processing, test-driven development.
2 Commits • 2 Features
Dec 1, 2024December 2024 performance summary for OpenSTEF/openstef: Delivered two time-series feature enhancements to improve model inputs and capture seasonal patterns. Implemented cyclic time features using sine/cosine transforms for time components (time of day, day of week, month) with new feature generation modules, and updated tests/data to reflect the new features. Added daylight terrestrial radiation feature by integrating an hourly radiation CSV (May 2023 to May 2024) with an ingestion/merge function, and refactoring tests/data to accommodate timezones and data integrity. No major bugs fixed this month. Overall impact: expanded feature set, potential uplift in forecasting accuracy and robustness, improved data quality and ETL consistency. Technologies/skills demonstrated: Python, time-series feature engineering, modular design, data integration, CSV handling, timezone-aware processing, test-driven development.
December 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVPython
Technical Skills
Data AnalysisData EngineeringFeature EngineeringNumPyPandasPythonTime Series AnalysisTime Series Data
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