Thermal Power Plant Load Sharing Optimization
Budget: $30 – $250 USD
Title:
Machine Learning Model for Optimized Load Sharing in Thermal Power Plant (10 Units)
Description:
Hello Freelancers,
I’m looking to develop a machine learning algorithm to optimize load sharing in a thermal power plant consisting of 8 thermal generator units.
The objective is to minimize fuel cost while satisfying the total load demand and generator constraints (like min/max limits).
The goal is to replace or speed up traditional methods (like iterative optimization solvers) with a machine learning model
that can quickly estimate the optimal power output for each generator unit.
What I Need:
Since I’m new to machine learning, I’m looking for someone who can:
Propose a suitable ML/DL model (e.g., feed-forward neural network, regression model, etc.).
Guide me or generate synthetic datasets for training and testing.
Train and validate the model to ensure it performs reliably.
Compare the ML model results with traditional optimization approaches (optional but preferred).
Provide complete source code (Python or MATLAB preferred).
Explain the process clearly, as this is also a learning experience for me.
Help me document the work in a clean and clear format (can be used to write a report or paper).
Deliverables:
Trained machine learning model for load sharing optimization.
Full source code with instructions to run and test.
Basic report or documentation.
Optional: Jupyter Notebook or MATLAB scripts for simulation and visualization.
Skills Required:
Machine Learning / Deep Learning
Power Systems Engineering (Economic Load Dispatch)
MATLAB or Python
Optimization Techniques
Clear Communication & Documentation Skills
Timeline:
Looking for first results or model setup within 2–3 weeks. Full project can be completed over 4 weeks depending on scope.
Machine Learning Model for Optimized Load Sharing in Thermal Power Plant (10 Units)
Description:
Hello Freelancers,
I’m looking to develop a machine learning algorithm to optimize load sharing in a thermal power plant consisting of 8 thermal generator units.
The objective is to minimize fuel cost while satisfying the total load demand and generator constraints (like min/max limits).
The goal is to replace or speed up traditional methods (like iterative optimization solvers) with a machine learning model
that can quickly estimate the optimal power output for each generator unit.
What I Need:
Since I’m new to machine learning, I’m looking for someone who can:
Propose a suitable ML/DL model (e.g., feed-forward neural network, regression model, etc.).
Guide me or generate synthetic datasets for training and testing.
Train and validate the model to ensure it performs reliably.
Compare the ML model results with traditional optimization approaches (optional but preferred).
Provide complete source code (Python or MATLAB preferred).
Explain the process clearly, as this is also a learning experience for me.
Help me document the work in a clean and clear format (can be used to write a report or paper).
Deliverables:
Trained machine learning model for load sharing optimization.
Full source code with instructions to run and test.
Basic report or documentation.
Optional: Jupyter Notebook or MATLAB scripts for simulation and visualization.
Skills Required:
Machine Learning / Deep Learning
Power Systems Engineering (Economic Load Dispatch)
MATLAB or Python
Optimization Techniques
Clear Communication & Documentation Skills
Timeline:
Looking for first results or model setup within 2–3 weeks. Full project can be completed over 4 weeks depending on scope.