In accordance with the optimization framework and parameterization methods pre-sented, future work can readily build upon the design to incorporate optimization strategies focused on energy saving and output controls for the heating system along with the windows. When updating constraints, future work may additionally factor in the time it takes the windows to open and close, about 1 second per 1% increase or decrease in opening extent.
Directly from this design contribution, a foreseeable next step is to build the software system interface for real-time control implementation and model develop-ment. With this set-up, the model may be built using optimized parameterization algorithms that better handle data gaps in the past several hours of server data, handling noise as necessary. The MPC algorithm may use that model to predict and implement controls within the next hour, even as the next model is trained incorporating room responses to those controls. This will transform the project from predictive to adaptive control, as the controller adapts its predictions based on up-to-date models [29]. Software integration will also be necessary to accommodate larger and more complex spaces within the whole building of HouseZero, and other building applications.
Real-time implementation will not only bring validation data closer in time to the training data, as compared to using a November 2018 model on March 2019 data.
It will also increase the frequency of updates in disturbance variables. Limited to 5 minute intervals during the control trials by manual implementation, updates to disturbance inputs may happen on the scale of seconds to a minute in the future software interface. These factors will likely contribute to a higher prediction accu-racy for the MPC algorithms, driving achievement of ambitious energy and thermal performance goals.
Beyond predictive and adaptive MPC, it will be fascinating to see future work with other system identification methods and control design approaches, branching into artificial neural networks and machine learning algorithms with the wealth of data available from building and city sensor networks.
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50 Chapter Ald´ıs Elfarsd´ottir
Appendix
A.1 System matrices, third iteration models
Fullrow-2nd Order Atrain-2nd Order Dimensions A coefficients state 1, state 2 state 1, state 2 2x2 state 1 0.9984, -0.0378 0.9985, -0.0562
state 2 1.5630e-4, 0.1769 0.0115, 0.3484
C coefficients state 1, state 2 state 1, state 2 1x2 room temp -136.1071, 1.1693 41.7302, -0.9131
K coefficients room temp room temp 2x1
state 1 -0.0089 0.0349
state 2 0.0337 -0.0730
Table A.1: A,C,K coefficients for states, response, and prediction error
Fullrow-2nd Order Atrain-2nd Order Dimensions B coefficients state 1, state 2 state 1, state 2 2x1 positR (%) -3e-6, -1e-4 2e-7, 2e-6
F coefficients state 1, state 2 state 1, state 2 2x13 positS (%) -4e-7, 8e-6 -1e-7, -4e-7
heatvalve (%) 4e-7, -1e-5 -2e-7, 6e-6 slab (oC) 1e-2, 1e-1 -5e-3, -1e-1 humidity (%) 2e-6, 1e-5 -7e-8, -4e-6 co2 (ppm) 5e-7, 4e-6 -5e-7, -1e-5
occupancy 2e-4, -9e-3 -8e-6, 5e-4
wind direct (o) 1e-7, 9e-7 -2e-9, 3e-7
rain -1e-6, -2e-4 9e-7, 4e-5
outdoor (oC) -2e-5, -3e-4 -5e-7, 2e-8 temp31 (oC) -4e-3, -5e-2 -6e-4, -1e-2 temp33 (oC) 7e-4, 8e-3 -1e-4, -3e-3 temp23 (oC) -3e-5, -3e-4 -4e-5, -8e-4 temp22 (oC) -6e-4, -7e-3 -8e-5, -2e-3
Table A.2: B,F coefficients for control and disturbances
Data-Driven Model Development for Model Predictive Building Control