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
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
Two methods are available for the optimisation problem;
- LBFGS
- Iterative Ensemble SMoother
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
-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
All libraries are included for your convenience.
Dr Clement Etienam- Research Officer-Machine Learning. Active Building Centre
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