architecture-building-systems/CityEnergyAnalyst
Jan 2025 – Oct 2025
5 Months active
Languages Used
PythonINICSV
Technical Skills
Energy Systems OptimizationParameterizationSoftware RefactoringThermal Network DesignBackend DevelopmentCode Refactoring
Mathias Niffeler
PROFILE
Mathias Niffeler
Contributed to the architecture-building-systems/CityEnergyAnalyst repository by developing and refining features for district energy system modeling, data ingestion, and visualization. Leveraged Python, Pandas, and NetworkX to overhaul data pipelines, implement robust optimization parameters, and enhance both 2D/3D visual analytics. Introduced dynamic network temperature handling and expanded component databases to improve simulation accuracy and scenario flexibility. Refactored code for maintainability, added comprehensive unit tests, and improved error handling in visualization scripts. Addressed cost modeling and data validation challenges, enabling more reliable forecasting and decision support. Focused on backend development, data engineering, and scientific computing to support urban energy planning workflows.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
17Total
Bugs
3
Commits
17
Features
7
Lines of code
2,121
Activity Months5
Work History
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for architecture-building-systems/CityEnergyAnalyst. The focus this month was delivering robust visualization of supply systems and strengthening the reliability of street-network graph corrections, while improving code quality and test coverage to support long-term maintenance and CI readiness. Key features delivered: - Supply System Graphics Visualization Robustness: enhanced the graphics script to handle folder naming changes, gracefully manage missing component icons, and correctly identify the latest optimization run, resulting in more accurate and resilient supply-system visualizations. - GraphCorrector Testing and Maintenance: introduced a comprehensive unit testing suite for the GraphCorrector class, covering connectivity, self-loops, merging near nodes, and connecting intersecting edges to improve reliability of street network graph corrections; also removed unused imports to fix test failures and clean up code quality. Major bugs fixed: - Stabilized supply systems graphics processing by accommodating folder naming variations and missing icons, reducing visualization failures in downstream dashboards. - Fixed test stability and reliability by removing unused imports and ensuring GraphCorrector-related tests pass consistently across environments. Overall impact and accomplishments: - Increased reliability and accuracy of visual analytics for city energy systems, enabling more confident decision-making and faster iterations for planning scenarios. - Strengthened code quality and maintainability with targeted refactoring and comprehensive unit tests, reducing technical debt and supporting CI/CD workflows. Technologies/skills demonstrated: - Python scripting and visualization pipelines, unit testing, and test-driven development. - Graph algorithms and data cleaning practices (GraphCorrector) and code quality improvements (removing unused imports, refactoring). - Attention to data integrity and user-facing visualization robustness, aligning with business value in urban energy analysis.
3 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for architecture-building-systems/CityEnergyAnalyst. The focus this month was delivering robust visualization of supply systems and strengthening the reliability of street-network graph corrections, while improving code quality and test coverage to support long-term maintenance and CI readiness. Key features delivered: - Supply System Graphics Visualization Robustness: enhanced the graphics script to handle folder naming changes, gracefully manage missing component icons, and correctly identify the latest optimization run, resulting in more accurate and resilient supply-system visualizations. - GraphCorrector Testing and Maintenance: introduced a comprehensive unit testing suite for the GraphCorrector class, covering connectivity, self-loops, merging near nodes, and connecting intersecting edges to improve reliability of street network graph corrections; also removed unused imports to fix test failures and clean up code quality. Major bugs fixed: - Stabilized supply systems graphics processing by accommodating folder naming variations and missing icons, reducing visualization failures in downstream dashboards. - Fixed test stability and reliability by removing unused imports and ensuring GraphCorrector-related tests pass consistently across environments. Overall impact and accomplishments: - Increased reliability and accuracy of visual analytics for city energy systems, enabling more confident decision-making and faster iterations for planning scenarios. - Strengthened code quality and maintainability with targeted refactoring and comprehensive unit tests, reducing technical debt and supporting CI/CD workflows. Technologies/skills demonstrated: - Python scripting and visualization pipelines, unit testing, and test-driven development. - Graph algorithms and data cleaning practices (GraphCorrector) and code quality improvements (removing unused imports, refactoring). - Attention to data integrity and user-facing visualization robustness, aligning with business value in urban energy analysis.
October 2025
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 — CityEnergyAnalyst (architecture-building-systems/CityEnergyAnalyst). Delivered key features and fixes that boost visualization fidelity, robustness, and user exploration capabilities. Pareto front visualization enhancements refactored path generation to align with new naming conventions; improved 2D/3D scatter plots to clearly distinguish current DES from non-dominated solutions; switched from Bézier curve fitting to alpha shape outlines for a more accurate solution-space representation; implemented consistent legend labeling. Robust handling of missing optimization results added friendly user messaging and direct plotting call behavior to avoid silent failures. Enabled interactive 2D plots in the browser, enabling in-app exploration of results. Together, these changes reduce time-to-insight, improve reliability of optimization results, and enhance the end-user experience for decision-makers.
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 — CityEnergyAnalyst (architecture-building-systems/CityEnergyAnalyst). Delivered key features and fixes that boost visualization fidelity, robustness, and user exploration capabilities. Pareto front visualization enhancements refactored path generation to align with new naming conventions; improved 2D/3D scatter plots to clearly distinguish current DES from non-dominated solutions; switched from Bézier curve fitting to alpha shape outlines for a more accurate solution-space representation; implemented consistent legend labeling. Robust handling of missing optimization results added friendly user messaging and direct plotting call behavior to avoid silent failures. Enabled interactive 2D plots in the browser, enabling in-app exploration of results. Together, these changes reduce time-to-insight, improve reliability of optimization results, and enhance the end-user experience for decision-makers.
August 2025
3 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for architecture-building-systems/CityEnergyAnalyst focused on delivering value through modeling enhancements and cost accuracy improvements for district energy systems (DES). The work strengthened decision-support capabilities, expanded scenario options, and improved overall cost precision for optimization workflows.
3 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for architecture-building-systems/CityEnergyAnalyst focused on delivering value through modeling enhancements and cost accuracy improvements for district energy systems (DES). The work strengthened decision-support capabilities, expanded scenario options, and improved overall cost precision for optimization workflows.
August 2025
February 2025
5 Commits • 1 Features
Feb 1, 2025February 2025 — CityEnergyAnalyst performance highlights: delivered a data ingestion overhaul enabling CSV-based storage for feedstocks and energy carriers, aligned the pipeline with the new CEA4 database format, and enhanced energy yield accuracy across latitudes. These changes improve data reliability, scalability for multi-file data sources, and support both decentralised and centralised optimisation workflows, delivering measurable business value and robust forecasting capabilities.
February 2025
5 Commits • 1 Features
Feb 1, 2025February 2025 — CityEnergyAnalyst performance highlights: delivered a data ingestion overhaul enabling CSV-based storage for feedstocks and energy carriers, aligned the pipeline with the new CEA4 database format, and enhanced energy yield accuracy across latitudes. These changes improve data reliability, scalability for multi-file data sources, and support both decentralised and centralised optimisation workflows, delivering measurable business value and robust forecasting capabilities.
January 2025
1 Commits • 1 Features
Jan 1, 2025Month: 2025-01 — Focused delivery on heating-system optimization improvements within CityEnergyAnalyst, emphasizing robustness and accurate energy-transport modeling across thermal networks. Implemented a Dynamic Network Temperature Parameter for Heating System Optimization to handle extreme supply-temperature variations. Refactored modules to improve energy carrier compatibility and temperature-range handling, enhancing optimization robustness and accuracy. This work aligns with business goals of energy efficiency, operator reliability, and cost savings in district-scale energy systems.
1 Commits • 1 Features
Jan 1, 2025Month: 2025-01 — Focused delivery on heating-system optimization improvements within CityEnergyAnalyst, emphasizing robustness and accurate energy-transport modeling across thermal networks. Implemented a Dynamic Network Temperature Parameter for Heating System Optimization to handle extreme supply-temperature variations. Refactored modules to improve energy carrier compatibility and temperature-range handling, enhancing optimization robustness and accuracy. This work aligns with business goals of energy efficiency, operator reliability, and cost savings in district-scale energy systems.
January 2025
Activity
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Quality Metrics
Correctness88.2%
Maintainability87.0%
Architecture86.4%
Performance77.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVINIPython
Technical Skills
Backend DevelopmentCode RefactoringData AnalysisData EngineeringData HandlingData ManagementData ValidationData VisualizationDatabase ManagementEnergy Systems OptimizationFile HandlingFile ManagementFile Path ManagementGeospatial AnalysisGraph Theory
Repositories Contributed To
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