Radu-C-Martin/Master-Project

Inter-seasonal GP MPC control for buildings

Identification and control code

This repository contains the code of the Notebooks and the server implementation of the GP-based MPC used as part of my Master Project

Inter-seasonal Performance of Gaussian Process-based Model Predictive Control of Buildings

which was done under the supervision of Prof. Colin Jones at the EPFL's Automatic Control Laboratory

Folder structure

The most important parts of the repository are presented in the tree below:

Master-Project
├── Data
│   ├── Experimental_data_WDB
│   ├── Good_CARNOT
│   ├── Luca_experimental_data
│   ├── Miscellaneous
│   └── Simulation_results
│       ├── First batch (explorative)
│       ├── Second batch
│       └── Third batch
│           ├── 10_SVGP_480pts_inf_window_12_averageYear_LinearKernel
│           ├── 1_SVGP_480pts_inf_window_12_averageYear
│           ├── 2_SVGP_480pts_inf_window_12_extremeSummer
│           ├── 3_SVGP_480pts_inf_window_12_extremeWinter
│           ├── 4_GP_480pts_12_averageYear
│           ├── 5_SVGP_480pts_480pts_window_12_averageYear
│           └── 6_SVGP_96pts_inf_window_12_averageYear
├── Literature
├── Matlab_scripts
├── Notebooks
│   ├── Images
│   └── Results
├── server
└── Simulink

Data folder

The Data folder contains all the experimental datasets (Luca_experimental_data), the CARNOT simulations using the experimental weather data (Experimental_data_WDB, used for validation of the CARNOT building), as well as the models and optimization results for the full-year simulations (in the sub-folder Simulation_results/Third batch)

Notebooks folder

The Notebooks folder contains all the relevant jupyter notebooks written while figuring out the structure of the MPC controller, the CARNOT system and their interface. These notebooks are also used to generate most of the plots used in the Master Project Report.

A quick summary of each notebook's uses is presented in the following list:

• 10_wdb_from_experimental_data.ipynb

  • Transforming experimental weather data to WDB

• 21_CARNOT_experimental_comparison.ipynb

  • Graphing external temp/ internal temp/ setpoint for all exps

• 30_gaussiandome_identification.ipynb

  • Gaussian Process identification for real building based on exp. data

• 38_gp_hyperparameter_estimation.ipynb

  • Gaussian Process identification on CARNOT experimental data
  • Error table generation
  • Training/test error plots
  • Multi-step ahead prediction + plots

• 39_svgp_hyperparameter_estimation.ipynb

  • SVGP model identification on CARNOT data
  • Error table generation
  • Training/test error plots
  • Multi-step ahead prediction + plots

• 42_casadi_callback_speed.ipynb

  • CasADi GP with callback on a GP of the CARNOT building

• 50_mpc_formulation.ipynb

  • Test of sample MPC with CasADi on a CARNOT GP

• 70_Server_result_analysis.ipynb

  • Plots for analyzing year-long simulation performance

Experiments:

• 31_gpflow_first_test.ipynb

  • Test of GPflow on a simple function

• 41_casadi_gp_test.ipynb

  • CasADi GP with callback on a simple function

server folder

The server folder contains the code for the Python server serving as the interface between the Simulink model and the MPC controller. The server.py script is responsible of reading the measurement values/weather forecast and sending the control signal to the CARNOT model. The measurement and control values are passed to/from and MPC object. The MPC itself is defined in controllers.py.

Required libraries

The python code uses several libraries for GP models, as well as general math/data operations:

• pandas and numpy are used for operating on arrays of data
• TensorFlow and GPflow are used for the GP model implementations and to wrap the CasADi callbacks as tf-functions.
• CasADi is the algorithmic differentation framework used to implement the Optimization Problem

There are also several libraries that are optional, and are not required for running the server code:

• matplotlib is used in the jupyter notebooks to plot the results

• bokeh is used in the jupyter notebooks where interactive plots were useful

• tqdm is used to display a progress bar in the notebooks where a lot of data is processed.