energy_data_science

Grad Course, CE 295 – Data Science for Energy

Course Description: Learned the fundamentals of data science methods for the design and operation of energy systems, including mathematical modeling & analysis, state estimation, optimization, machine learning, and optimal control.

File Descriptions:

1. hw1.ipynb – battery modeling, analysis, and simulation, utilizing state-space representation and linearization
2. hw2.ipynb – state estimation in geothermal heat pump drilling, utilizing a luenberger observer, kalman filter, and extended kalman filter
3. hw3.ipynb – optimization of economic dispatch in distribution feeders with renewables, utilizing convex programming and robust constraints.
4. hw4.ipynb – time series forecasing of residential electricity power consumption, utilizing average, ARX, and neural network models
5. Co-Optimization.ipynb – code for co-optimization of a water heater, HVAC, and solar panels under varying tariff regimes
6. final_presentation.pdf – ppt presentation of Co-Optimization.ipynb findings
7. final_report.pdf – written report of co-optimization methods and results