Energy Management Using Deep Learning-Based Model Predictive Control (MPC)

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Learn how to control a house heating system using nonlinear model predictive control (MPC) with a data-driven prediction model. In this control problem, try to minimize energy costs while maintaining the house temperature within a certain temperature range by utilizing MPC’s preview and constraint handling capabilities. You will learn how you can train a neural state-space prediction model just in a few lines of code and then use the trained model as the internal prediction model in the nonlinear MPC controller.

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Thank you for sharing. However, I have a question, please. I am currently implementing an MPC to control the temperature inside a room. To model the system, I used a neural network that takes as input a window of data (disturbance_w, control_w, output_w) to predict the output over a prediction horizon. Then, I use these predictions to calculate an objective function in order to obtain the first command to apply to my system to get the first output. For this, I use scipy, but the control proposed by this library remains constant regardless of the output values (the output does not follow the reference). Do you have any advice to improve this?

amel

So MPC saved money for that brief period after the price of electricity went up and after that it would be the same as any other control system?
It would be huge savings if considered across all of households but for singular examples is it really worth the money?

jakovbilic

Very nice demonstration thanks. What are those 'future sample extractor' blocks in the Simulink model? I can't find any documentation for them or see them in my Simulink app (R2021b). Can anyone point me to the documentation on this?

BillTubbs

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