https://github.com/clementetienam/data_driven_mpc_controller_using_ccr

data-driven-mpc machine-learning mixture-of-experts supervised-learning time-series xgboost-model

Last synced: 8 months ago
JSON representation

Host: GitHub
URL: https://github.com/clementetienam/data_driven_mpc_controller_using_ccr
Owner: clementetienam
License: mit
Created: 2020-12-15T11:28:42.000Z (almost 5 years ago)
Default Branch: main
Last Pushed: 2020-12-15T11:50:51.000Z (almost 5 years ago)
Last Synced: 2024-12-04T06:13:16.241Z (10 months ago)
Topics: data-driven-mpc, machine-learning, mixture-of-experts, supervised-learning, time-series, xgboost-model
Language: MATLAB
Homepage:
Size: 11.9 MB
Stars: 3
Watchers: 2
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

          # Data_Driven_MPC_Controller_Using_CCR

------------------------------------------------------------------------------

Box Configuration

Inputs:

Environment:Site Outdoor Air Drybulb Temperature [C]

Environment:Site Outdoor Air Wetbulb Temperature [C]

Environment:Site Outdoor Air Relative Humidity [%]

Environment:Site Wind Speed [m/s]

Environment:Site Wind Direction [deg]

Environment:Site Horizontal Infrared Radiation Rate per Area [W/m2]

Environment:Site Diffuse Solar Radiation Rate per Area [W/m2]

Environment:Site Direct Solar Radiation Rate per Area [W/m2]

THERMAL ZONE: BOX:Zone Outdoor Air Wind Speed [m/s]

Outputs:

THERMAL ZONE: BOX:Zone Mean Air Temperature [C]

------------------------------------------------------------------------------

GSHP configuration

Inputs:

Environment:Site Outdoor Air Drybulb Temperature [C]

Environment:Site Outdoor Air Wetbulb Temperature [C]

Environment:Site Outdoor Air Relative Humidity [%]

Environment:Site Wind Speed [m/s]

Environment:Site Wind Direction [deg]

Environment:Site Horizontal Infrared Radiation Rate per Area [W/m2]

Environment:Site Diffuse Solar Radiation Rate per Area [W/m2]

Environment:Site Direct Solar Radiation Rate per Area [W/m2]

THERMAL ZONE: BOX:Zone Outdoor Air Wind Speed [m/s]

GSHPCLG:Heat Pump Electric Power [W]

GSHPCLG:Heat Pump Source Side Inlet Temperature [C]

GSHPHEATING:Heat Pump Electric Power [W]

GSHPHEATING:Heat Pump Source Side Inlet Temperature [C]

Outputs:

THERMAL ZONE: BOX:Zone Mean Air Temperature [C]

Data Driven MPC Approach. Online approach

steps:

    1) Predict room temperature at time t given current weather states

    2) Optimise for control at time t to reference room temperature

    3) Predict room temperature at time t+1 using temperature at time t

    4) Predict weather states for t+1 using temperature at t+1 (gotten from 3)

    4) Set for next evolution, temperature at t= temperature at t+1

      (prior(t)= posterior(t+1))

    

mathematically;    

y=room temperature

X=weather states

u=control for GSHP pump

input: u(t-1)= initial guess,X(t-1)(= Known), r for all t(= known), f1, f2,g (= Learned),

y(t-1)(=Infered from y(t-1)=f1(X(t-1))+e )

g=LSTM machine

f1=States to output (room temperature) machine (Pure weather conditions)

f2=Augmented states (with control inputs) to room temperature

set:

y(1)=y(t-1) 

X(1)=X(t-1) 

u(1)=u(t-1) 

Do t= 1: Horizon:

    y(t+1)=g(y(t))+n # Predict the future output given present output

    y(t)=f1(X(t))+e # Predict current output with current states

    ybig(t,:)=y(t)

    u(opt)=argmin||r(t)-f2(X(t);u(t),X(t))||+z # Optimise the control at time t

    ubig(t,:)=u(opt)

    Xbig(t,:)=X

    

    X(opt)=argmin||y(t+1)-f1(X(t)||+z # Optimise the state at time t+1

    set X(t)= X(t+1)=X(opt)

    set u(t)= u(t+1)=u(opt)

    

    

End Do

Getting Started:

---------------------------------

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. 

See deployment for notes on how to deploy the project on a live system.

-Run the script TRAINING.m to learn the machines of the LSTM, states to room temperature and states+contoller (GSHP pump) to temperature

-Optimise the controller input using the DD_MPC.m for Batch optimisation or DD_MPC_SEQUENTIAL_2 for sequential optimsation

Prerequisites:

-------------------------------

MATLAB 2018 upwards

Methods:

-------------------------------

Two methods are available for the optimisation problem;

- LBFGS

- Iterative Ensemble SMoother

Datasets

-----------------------------

Both datasets can be found i the Data directory

- The Weather dataset for 2 years with a daily frequency is called "Box.csv"

-The training dataset for the controller is found in the "GSHP.csv" FILE

Running the Numerical Experiment:

-----------------------------

-Run the script TRAINING.m to learn the machines of the LSTM, states to room temperature and states+contoller (GSHP pump) to temperature

-Optimise the controller input using the DD_MPC.m for Batch optimisation or DD_MPC_SEQUENTIAL_2 for sequential optimsation

Dependencies

----------------------------

All libraries are included for your convenience.

Manuscript

-----------------------------

-

Author:

--------------------------------

Dr Clement Etienam- Research Officer-Machine Learning. Active Building Centre

Acknowledgments:

------------------------------

References:

----------------------------

[1] Luca Ambrogioni, Umut Güçlü, Marcel AJ van Gerven, and Eric Maris. The kernel mixture network: A non-parametric method for conditional density estimation 

of continuous random variables. arXiv preprint arXiv:1705.07111, 2017.

[2] Christopher M Bishop. Mixture density networks. 1994.

[3] Isobel C. Gormley and Sylvia Frühwirth-Schnatter. Mixtures of Experts Models. Chapman and Hall/CRC, 2019.

[4] R.B. Gramacy and H.K. Lee. Bayesian treed Gaussian process models with an application to computer modeling. Journal of the American Statistical Association, 103(483):1119–1130,

2008.

[5] Robert A Jacobs, Michael I Jordan, Steven J Nowlan, Geoffrey E Hinton, et al. Adaptive

mixtures of local experts. Neural computation, 3(1):79–87, 1991.

2

[6] Michael I Jordan and Robert A Jacobs. Hierarchical mixtures of experts and the em algorithm.

Neural computation, 6(2):181–214, 1994.

[7] Trung Nguyen and Edwin Bonilla. Fast allocation of gaussian process experts. In International

Conference on Machine Learning, pages 145–153, 2014.

[8] Carl E Rasmussen and Zoubin Ghahramani. Infinite mixtures of gaussian process experts. In

Advances in neural information processing systems, pages 881–888, 2002.

[9] Tommaso Rigon and Daniele Durante. Tractable bayesian density regression via logit stickbreaking

priors. arXiv preprint arXiv:1701.02969, 2017.

[10] Volker Tresp. Mixtures of gaussian processes. In Advances in neural information processing

systems, pages 654–660, 2001.

[11] Lei Xu, Michael I Jordan, and Geoffrey E Hinton. An alternative model for mixtures of experts.

[12] Rasmussen, Carl Edward and Nickisch, Hannes. Gaussian processes for machine learning (gpml) toolbox. The

Journal of Machine Learning Research, 11:3011–3015, 2010

[13] David E. Bernholdt, Mark R. Cianciosa, David L. Green, Jin M. Park, Kody J. H. Law, and

Clement Etienam. Cluster, classify, regress: A general method for learning discontinuous functions. Foundations of Data Science, 

1(2639-8001-2019-4-491):491, 2019.

[14] Clement Etienam, Kody Law, Sara Wade. Ultra-fast Deep Mixtures of Gaussian Process Experts. arXiv preprint arXiv:2006.13309, 2020.

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

https://github.com/clementetienam/data_driven_mpc_controller_using_ccr

Awesome Lists containing this project

README