
Over 15 months, contributed to cpmodel/FTT_StandAlone by developing and refining advanced energy and transport system modeling features. Focused on backend development and data analysis, the work included implementing dynamic cost and emission calculations, integrating policy and carbon pricing logic, and enhancing scenario management through API endpoints. Leveraged Python, NumPy, and Numba to optimize performance, vectorize computations, and improve code maintainability. Addressed data quality and validation, modernized input handling, and automated documentation workflows. These efforts improved model accuracy, reliability, and scalability, enabling more robust decision support for planning, forecasting, and policy analysis in complex energy and transport domains.
April 2026 — cpmodel/FTT_StandAlone: Focused on stabilizing data quality, modernizing inputs, and refining statistical calculations to improve reliability and business value of the FTT model. Key deliverables include (1) calibration gamma data issue fix using a logit transformation and MWKA set to -1 during calibration to prevent interference with gamma optimization, addressing data artifacts during the transition from generation to capacity; (2) frontend data source modernization to remove reliance on outdated Excel files, improving data handling and user experience; (3) improved logit-share trend slope calculations with adaptive historical window and a fixed simulation window to minimize standard error and enhance robustness for high-variability technologies. Together, these changes strengthen data integrity, model stability, and decision-support capabilities for planning and policy analyses.
April 2026 — cpmodel/FTT_StandAlone: Focused on stabilizing data quality, modernizing inputs, and refining statistical calculations to improve reliability and business value of the FTT model. Key deliverables include (1) calibration gamma data issue fix using a logit transformation and MWKA set to -1 during calibration to prevent interference with gamma optimization, addressing data artifacts during the transition from generation to capacity; (2) frontend data source modernization to remove reliance on outdated Excel files, improving data handling and user experience; (3) improved logit-share trend slope calculations with adaptive historical window and a fixed simulation window to minimize standard error and enhance robustness for high-variability technologies. Together, these changes strengthen data integrity, model stability, and decision-support capabilities for planning and policy analyses.
March 2026 monthly summary for cpmodel/FTT_StandAlone: delivered core enhancements to gamma value management and automation, and extended the FTT-Tr model with per-country historical data support and multiple start dates. These updates improve data reliability, automation maintainability, and regional modeling flexibility, enabling faster scenario analysis and more accurate gamma-based decisions..
March 2026 monthly summary for cpmodel/FTT_StandAlone: delivered core enhancements to gamma value management and automation, and extended the FTT-Tr model with per-country historical data support and multiple start dates. These updates improve data reliability, automation maintainability, and regional modeling flexibility, enabling faster scenario analysis and more accurate gamma-based decisions..
February 2026 monthly summary for cpmodel/FTT_StandAlone: Delivered a new Gamma Value Automation feature with API endpoints to initialize, run, save, and update gamma values, enabling dynamic gamma scenario management. The work included targeted refactoring to improve readability and maintainability of the automation model and gamma process. Commit activity focused on implementing the feature and cleaning up the codebase, anchoring the effort with notable changes: 6a0e65742ba5e7b8136f33ec0e97568d720f30d3 (Newly designed gamma value automation); 89568a7e55322f0ee254e5884d1c411e2cdad97e (Cleanup code — better variable names, remove unnecessary functions); 2324412c9aaea8b83dbcec394c861e7d7ea474e2 (Clean up code). These changes improved testability, reduced complexity, and prepared groundwork for broader automation scenarios.
February 2026 monthly summary for cpmodel/FTT_StandAlone: Delivered a new Gamma Value Automation feature with API endpoints to initialize, run, save, and update gamma values, enabling dynamic gamma scenario management. The work included targeted refactoring to improve readability and maintainability of the automation model and gamma process. Commit activity focused on implementing the feature and cleaning up the codebase, anchoring the effort with notable changes: 6a0e65742ba5e7b8136f33ec0e97568d720f30d3 (Newly designed gamma value automation); 89568a7e55322f0ee254e5884d1c411e2cdad97e (Cleanup code — better variable names, remove unnecessary functions); 2324412c9aaea8b83dbcec394c861e7d7ea474e2 (Clean up code). These changes improved testability, reduced complexity, and prepared groundwork for broader automation scenarios.
Monthly summary for 2025-12 for cpmodel/FTT_StandAlone: Delivered core updates to resource mapping for the new generation, removed redundant gamma variable, refined performance-oriented documentation and SCOP guidance, and strengthened developer tooling with automated API references. Also implemented robust bug fixes and quality improvements across configuration, environment, and UI docs to improve maintainability, onboarding, and production-readiness.
Monthly summary for 2025-12 for cpmodel/FTT_StandAlone: Delivered core updates to resource mapping for the new generation, removed redundant gamma variable, refined performance-oriented documentation and SCOP guidance, and strengthened developer tooling with automated API references. Also implemented robust bug fixes and quality improvements across configuration, environment, and UI docs to improve maintainability, onboarding, and production-readiness.
November 2025 monthly summary for cpmodel/FTT_StandAlone. Delivered critical accuracy enhancements to cost and energy calculations by refining resource accounting and energy output logic. Specifically, removed unused resource classifications and corrected heat pump efficiency double counting in HJET, improving reliability of cost projections and energy forecasts for budgeting and reporting.
November 2025 monthly summary for cpmodel/FTT_StandAlone. Delivered critical accuracy enhancements to cost and energy calculations by refining resource accounting and energy output logic. Specifically, removed unused resource classifications and corrected heat pump efficiency double counting in HJET, improving reliability of cost projections and energy forecasts for budgeting and reporting.
In September 2025, cpmodel/FTT_StandAlone delivered key standalone readiness and performance improvements: FTT model cleanup and module configuration for standalone runs; power system modeling enhancements with performance optimizations; and a bug fix to elapsed time calculation for accurate run duration reporting. These changes improve reliability, speed, and maintainability of standalone simulations and provide clearer, vectorized computations for emissions.
In September 2025, cpmodel/FTT_StandAlone delivered key standalone readiness and performance improvements: FTT model cleanup and module configuration for standalone runs; power system modeling enhancements with performance optimizations; and a bug fix to elapsed time calculation for accurate run duration reporting. These changes improve reliability, speed, and maintainability of standalone simulations and provide clearer, vectorized computations for emissions.
August 2025 monthly summary for cpmodel/FTT_StandAlone: Delivered a cohesive set of performance, reliability, and maintainability improvements, with a focus on documentation, speed, and smarter generation logic. The work enhances onboarding and reduces runtime, enabling faster iterations and more accurate planning for end users and stakeholders.
August 2025 monthly summary for cpmodel/FTT_StandAlone: Delivered a cohesive set of performance, reliability, and maintainability improvements, with a focus on documentation, speed, and smarter generation logic. The work enhances onboarding and reduces runtime, enabling faster iterations and more accurate planning for end users and stakeholders.
Performance summary for July 2025 (2025-07) focusing on cpmodel/FTT_StandAlone: Key features delivered - Investment cost calculation improvements across Freight, Heat, Power, and Transport; enabled Heat, Transport, and Freight modules in settings for simulation. This enables end-to-end cost assessment in more scenarios and accelerates decision-making for capital allocation. (Commit: 9e91aceb949e2df8ef2e2a9493c98036945bfd78) - Market share and regulatory policy performance improvements and refactoring: major performance enhancements, vectorized computations, and JIT/Numba optimizations; country-specific substitutions and broader code cleanliness to support faster scenario runs and more reliable policy analysis. Representative commits include start of shares refactoring and subsequent optimization work (e.g., 698e2849ae84..., 79e10c417f58b9..., 3e473044da6c1df5...). - Early scrapping costs readability improvement: renamed internal variables for clarity without changing functionality, improving maintainability (commit 5cbdca9ede8af60d3858b3604c71cc92f83830ae). Major bugs fixed - LCOH calculation bug fixes: corrected incorrect LCOH calculation related to tlcohg and gamma multiplier, ensuring cost outputs align with inputs. - Fixes to fuel costs: corrected double-counting in fuel costs standard deviation to prevent overstated variability. Overall impact and accomplishments - Improved accuracy of lifecycle cost estimates and investment decisions across multiple modules, enabling more reliable long-term planning. - Substantial gains in simulation performance and scalability via vectorization and JIT optimizations, reducing run times for large scenario analyses. - Code quality improvements and clearer maintainability through targeted refactors and naming improvements. Technologies/skills demonstrated - Python performance engineering: vectorization, JIT compilation (Numba), and optimized computation paths. - Refactoring discipline: modularization, readability enhancements, and settings-driven module enabling. - End-to-end costing discipline: alignment of LCOH, investment costs, and fuel cost calculations across Freight, Heat, Power, and Transport.
Performance summary for July 2025 (2025-07) focusing on cpmodel/FTT_StandAlone: Key features delivered - Investment cost calculation improvements across Freight, Heat, Power, and Transport; enabled Heat, Transport, and Freight modules in settings for simulation. This enables end-to-end cost assessment in more scenarios and accelerates decision-making for capital allocation. (Commit: 9e91aceb949e2df8ef2e2a9493c98036945bfd78) - Market share and regulatory policy performance improvements and refactoring: major performance enhancements, vectorized computations, and JIT/Numba optimizations; country-specific substitutions and broader code cleanliness to support faster scenario runs and more reliable policy analysis. Representative commits include start of shares refactoring and subsequent optimization work (e.g., 698e2849ae84..., 79e10c417f58b9..., 3e473044da6c1df5...). - Early scrapping costs readability improvement: renamed internal variables for clarity without changing functionality, improving maintainability (commit 5cbdca9ede8af60d3858b3604c71cc92f83830ae). Major bugs fixed - LCOH calculation bug fixes: corrected incorrect LCOH calculation related to tlcohg and gamma multiplier, ensuring cost outputs align with inputs. - Fixes to fuel costs: corrected double-counting in fuel costs standard deviation to prevent overstated variability. Overall impact and accomplishments - Improved accuracy of lifecycle cost estimates and investment decisions across multiple modules, enabling more reliable long-term planning. - Substantial gains in simulation performance and scalability via vectorization and JIT optimizations, reducing run times for large scenario analyses. - Code quality improvements and clearer maintainability through targeted refactors and naming improvements. Technologies/skills demonstrated - Python performance engineering: vectorization, JIT compilation (Numba), and optimized computation paths. - Refactoring discipline: modularization, readability enhancements, and settings-driven module enabling. - End-to-end costing discipline: alignment of LCOH, investment costs, and fuel cost calculations across Freight, Heat, Power, and Transport.
June 2025 highlights for cpmodel/FTT_StandAlone: delivered a core refactor of emission and fuel calculations with centralized emission corrections for consistency, resulting in more accurate and maintainable modeling. Implemented performance improvements and code-path cleanup to achieve faster execution. Completed data management and naming cleanup to improve data quality and governance, including CSV-based handling for classification_titles and tidy masterfiles. Integrated updated FTT:Tr data and historical estimates to enhance forecasting fidelity. Added TJET integration updates and related modeling enhancements (including a 2D mandate variable in BHTC) to broaden scenario coverage. Addressed critical bugs to stabilize the codebase, including TJET biofuel fixes, paste-removal revert, and merge-message improvements.
June 2025 highlights for cpmodel/FTT_StandAlone: delivered a core refactor of emission and fuel calculations with centralized emission corrections for consistency, resulting in more accurate and maintainable modeling. Implemented performance improvements and code-path cleanup to achieve faster execution. Completed data management and naming cleanup to improve data quality and governance, including CSV-based handling for classification_titles and tidy masterfiles. Integrated updated FTT:Tr data and historical estimates to enhance forecasting fidelity. Added TJET integration updates and related modeling enhancements (including a 2D mandate variable in BHTC) to broaden scenario coverage. Addressed critical bugs to stabilize the codebase, including TJET biofuel fixes, paste-removal revert, and merge-message improvements.
May 2025 monthly summary for cpmodel/FTT_StandAlone focusing on the key features delivered, major bugs fixed, and overall impact. The month saw a mix of feature refinements, data/model maintenance, and targeted bug fixes that improved robustness, accuracy, and business value of the transport decision-support model.
May 2025 monthly summary for cpmodel/FTT_StandAlone focusing on the key features delivered, major bugs fixed, and overall impact. The month saw a mix of feature refinements, data/model maintenance, and targeted bug fixes that improved robustness, accuracy, and business value of the transport decision-support model.
April 2025 monthly summary for cpmodel/FTT_StandAlone. Focused on delivering robust performance enhancements, policy-aware modeling, and data integrity improvements to enable faster iteration and accurate scenario analysis for heat and power systems.
April 2025 monthly summary for cpmodel/FTT_StandAlone. Focused on delivering robust performance enhancements, policy-aware modeling, and data integrity improvements to enable faster iteration and accurate scenario analysis for heat and power systems.
Concise month summary for 2024-10 focusing on delivering business value through data quality improvements, user experience enhancements, and data inputs reliability for cpmodel/FTT_StandAlone. Key outcomes include precise UX messaging for missing CSV files, strict NaN data validation with explicit errors to aid debugging, and updated master data files to ensure input accuracy. Collectively, these work items reduce support time, minimize downstream errors, and support reliable analytics and decision-making.
Concise month summary for 2024-10 focusing on delivering business value through data quality improvements, user experience enhancements, and data inputs reliability for cpmodel/FTT_StandAlone. Key outcomes include precise UX messaging for missing CSV files, strict NaN data validation with explicit errors to aid debugging, and updated master data files to ensure input accuracy. Collectively, these work items reduce support time, minimize downstream errors, and support reliable analytics and decision-making.
July 2024 monthly summary: Delivered feature alignment for LCOE calculation in cpmodel/FTT_StandAlone by removing marginal storage costs to align with E3ME-FTT; this change prepares MEWP implications for future calculations and improves cost estimation accuracy used in investment decisions. Commits include a96a24a27640a5ae986f027f7082963fedea3b63 ('correct MECC; no marginal storage costs').
July 2024 monthly summary: Delivered feature alignment for LCOE calculation in cpmodel/FTT_StandAlone by removing marginal storage costs to align with E3ME-FTT; this change prepares MEWP implications for future calculations and improves cost estimation accuracy used in investment decisions. Commits include a96a24a27640a5ae986f027f7082963fedea3b63 ('correct MECC; no marginal storage costs').
June 2024 monthly summary for cpmodel/FTT_StandAlone: Delivered significant enhancements to core model accuracy and cost calculation, improved demand estimation fidelity, and strengthened robustness and maintainability. Implemented targeted data handling improvements for MSSC/MLSC storage and LCOE calculations, refined data export workflows, and carried out comprehensive code cleanup. These changes reduce external input dependencies, improve forecast reliability, and enable more accurate LCOE-based decision making for storage and generation resources, while keeping configurations compatible with existing features.
June 2024 monthly summary for cpmodel/FTT_StandAlone: Delivered significant enhancements to core model accuracy and cost calculation, improved demand estimation fidelity, and strengthened robustness and maintainability. Implemented targeted data handling improvements for MSSC/MLSC storage and LCOE calculations, refined data export workflows, and carried out comprehensive code cleanup. These changes reduce external input dependencies, improve forecast reliability, and enable more accurate LCOE-based decision making for storage and generation resources, while keeping configurations compatible with existing features.
May 2024 monthly summary for cpmodel/FTT_StandAlone. Focused on delivering core features, stabilizing the codebase, and extending modeling capabilities to improve accuracy, performance, and business decision support. Key work spanned MEWW exploration with corrections and redefinition of MEWC/MECW; MKWA implementation; interface updates across MEWDX/MEWLX and MCFCX; data handling and performance improvements. Also included targeted bug fixes and quality improvements to reduce risk in production. Major contributions drive model fidelity, reliability, and faster iteration cycles, enabling more accurate cost and demand scenarios for strategic planning and operational optimization.
May 2024 monthly summary for cpmodel/FTT_StandAlone. Focused on delivering core features, stabilizing the codebase, and extending modeling capabilities to improve accuracy, performance, and business decision support. Key work spanned MEWW exploration with corrections and redefinition of MEWC/MECW; MKWA implementation; interface updates across MEWDX/MEWLX and MCFCX; data handling and performance improvements. Also included targeted bug fixes and quality improvements to reduce risk in production. Major contributions drive model fidelity, reliability, and faster iteration cycles, enabling more accurate cost and demand scenarios for strategic planning and operational optimization.

Overview of all repositories you've contributed to across your timeline