Has someone developed an model predictive controller while working with IDA-ICE Python API?

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Janak Trivedi

Rakesh Ramesh

In a model predictive control (MPC) problem, you have a prediction model that predicts future behavior based on current inputs and past outputs. You also have a cost function that you want to minimize, which represents the objective of your control system. The optimization problem involves finding the optimal control inputs (in this case, temperature setpoints) that minimize the cost function over a specified prediction horizon.

To solve this optimization problem, you can use a optimization library such as scipy.optimize. In particular, the minimize function in scipy.optimize can be used to minimize a cost function with respect to some variables. The cost function should represent the objective you want to optimize, and the variables should represent the control inputs (in this case, temperature setpoints).

Here's a general outline of the steps you would need to follow to implement the MPC:

Define the prediction model: This should be a function that takes in the current state and control inputs and outputs the predicted future state over the prediction horizon. You already have this part done with IDA-ICE.

Define the cost function: This should be a function that takes in the predicted future state and outputs the cost over the prediction horizon. The cost function should represent the objective you want to optimize.

Define the bounds and constraints: This includes any bounds and constraints on the control inputs (in this case, temperature setpoints). For example, you might want to ensure that the setpoints are within a certain range.

Call the minimize function from scipy.optimize: Pass in the cost function, the initial guess for the control inputs, and the bounds and constraints. The minimize function will then return the optimal control inputs that minimize the cost function.

Update the control inputs: Use the optimal control inputs returned by the minimize function to update the temperature setpoints. Repeat this process at each time step to continuously optimize the control inputs.

I hope this helps!

Qamar Ul Islam

Rakesh Ramesh To begin, you may optimize using the scipy.optimize.minimize method. You may define your goal function (for example, cost minimization), as well as its restrictions and boundaries. This function's output can be used to update the setpoint.

You may also address your optimization challenge with popular optimization packages such as cvxpy or pulp.

The IDAICE Python library may be used to interact with the IDA-ICE API. This package makes it simple to perform energy simulations and retrieve data from IDA-ICE. You may develop a script that will adjust the setpoint at regular intervals, then re-run the simulation and update the setpoint depending on the optimization results.

It is advised that you examine the IDA-ICE and IDAICE manuals for further information and examples on how to use them.

Md Abdur Rouf

Yes, it is possible to develop a model predictive controller (MPC) using the IDA-ICE Python API. IDA-ICE is a platform for modeling, simulation, and optimization of energy systems, and the Python API provides a way to interact with the platform programmatically.

To develop an MPC using the IDA-ICE Python API, you would need to implement the following steps:

Define the system dynamics and constraints as a mathematical model.

Formulate the MPC optimization problem, including the prediction horizon and objective function.

Use the IDA-ICE Python API to interface with the optimization solver and implement the MPC algorithm.

Integrate the MPC with the system being controlled, using the Python API to read measurement data and update control inputs in real-time.

Validate the MPC by testing it on simulated or real system data and comparing the results to the desired behavior.

It is worth noting that there may already be libraries or tools available that can help with the implementation of MPC in IDA-ICE, so it may be worth checking existing resources before starting to develop your own solution from scratch.

Mohamed-Mourad Lafifi

Hi,

I suggest you see the links on the topic.

http://publications.rwth-aachen.de/record/814409/files/814409.pdf

https://positive-energy-buildings.eu/fileadmin/user_upload/Deliverable_3.4_EXCESS.pdf

https://eprints.whiterose.ac.uk/190424/1/buildings-12-01284-v2.pdf

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Best regards