Abhijith SivaprasadanThermal engineering & energy systems
Abhijith Sivaprasadan

Abhijith SivaprasadanM.Sc. candidate at KTH · Stockholm, Sweden

Energy Modelling / Portfolio

Modelling energy. Informing decisions.

I build and analyse models of heat, power, storage and industrial energy use. My work connects technology assumptions, network constraints and operating decisions through optimisation, scenario analysis and transparent reporting.

01 / Selected work

Work behind this track.

Complete project library →

A focused selection. Open a case study for methods, results and limitations, or follow the skill dossiers below for the complete related record.

Independent project · 2026

OpenSteamOpt · Steam & power RTO

An open-source two-boiler steam-and-power twin with a carbon-aware Pyomo/HiGHS scheduler, OpenModelica FMI 2.0 simulation and a local Streamlit advisory GUI. Synthetic and uncalibrated; not an ABB product or live control system.

Python · Modelica · Pyomo · HiGHS · FMI/FMPy · Streamlit · pytest

Independent project · 2026

GB-FLEXABM · Electricity investment

Local electricity-investment GUI and shared Pyomo/HiGHS planner, with audited market coverage and 175 extracted public input references. Synthetic and uncalibrated; normalization, price-target decisions and weather conversion still gate historical fitting.

Python · Pyomo · HiGHS · Streamlit · NumPy · pytest

Independent project · 2026

PyPSA-NL · Grid flexibility

How do corridor limits and flexibility change dispatch? A Netherlands-inspired screening model with hand-checkable congestion and storage tests. Synthetic topology; not a validated Dutch grid.

PyPSA · Linopy · HiGHS · Streamlit · Python

Independent project

PyNEXUS · Multi-vector dispatch

Electricity, hydrogen and heat dispatch (Pyomo/HiGHS) on a real fetched year of ERA5 wind, with independent verification on every run and an interactive Streamlit GUI.

Pyomo · HiGHS · Python · Streamlit · Hydrogen

Independent project

Heating Demand Forecasting

Day-ahead building heating-demand forecasting using weather and temporal features, baseline models and neural networks.

Python · Machine learning · Time series

Internship methodology · Dec 2024 - Jun 2025

Alleima Industrial Energy Performance Mapping

Industrial energy performance methodology covering KPI/EnPI design, metering readiness, load-driver logic, deviation detection and regulatory comparison for decarbonization decisions.

Python · ISO 50001 · EU EED · Excel

02 / Experience

Relevant professional practice.

Full experience record →

Internship · Dec 2024 - Jun 2025

Energy Efficiency Intern · Alleima

Developed and independently validated a quantitative methodology for industrial energy performance mapping, covering KPI/EnPI design, load-driver logic, metering-gap assessment, deviation detection from noisy operational data and regulatory comparison. The work was desk-based method development and decision-support framing rather than hands-on plant execution, with electrical utilities and compressed air treated as relevant electrification pathways. Proprietary site detail is not republished.

Python · ISO 50001 · EU EED · KPI/EnPI Design · Energy Performance Mapping · Industrial Energy Analysis · Structured Reporting

03 / Skills & evidence

Go deeper into each area.

Each skill opens its own page with all related public projects, roles, coursework, training and supporting material.

04 / Education

Academic foundation.

Coursework & descriptions →

Academic foundation · 2023–2026 · KTH Royal Institute of Technology

M.Sc. Sustainable Energy Engineering

Master’s programme in heat and power, energy systems and numerical methods. Thesis completed; 115 hp recorded in the May 2026 transcript.

Academic foundation · 2017–2021 · College of Engineering Perumon / APJ Abdul Kalam Technological University

B.Tech Mechanical Engineering

Mechanical engineering foundation with coursework, design competitions and the final-year interactive robot project.

05 / Supporting material

Profiles & further reading.

Scope & scientific limitations

OpenSteamOpt is a synthetic educational steam/power twin, not live control or an ABB product. GB-FLEXABM is synthetic and uncalibrated; PyPSA-NL uses a synthetic topology. None is a validated operational or national-system model. PyNEXUS has a synthetic 168-hour reference, not an annual-run claim. Alleima work was desk-based methodology, not implemented plant savings. Python applications run locally, not on GitHub Pages.

Source case studies remain authoritative. Private inputs and restricted project details are not published.

Let’s connect

Continue the conversation.

For energy-system modelling, optimisation and energy-analysis roles.

abhijithsivaprasadan@gmail.com

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