Added README file

README.md

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+# 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](https://www.epfl.ch/labs/la/)
+
+## Folder structure
+
+The most important parts of the repository are presented in the tree below:
+
+```shell
+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](https://github.com/Radu-C-Martin/Master-Thesis).
+
+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.