PhD position Quantifying the sensitivity of thermal comfort control to system uncertainty - Genk, België - EnergyVille

Beschrijving

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Beschrijving

Advanced control strategies, and model predictive control (MPC) in particular, are gaining widespread interest for building energy management systems, since they can systematically save energy and/or costs with simultaneous thermal comfort improvement, as well as adapt the energy demand according to the available renewable/residual supply. The performance of any MPC strategy is dependent on the accuracy of the model describing the system and on the quality of the forecasts of disturbances, such as weather and occupant behaviour. Deviating model parameters and inaccurate disturbance predictions are shown to give rise to increased energy costs and comfort violations if not properly accounted for, and require real-time corrective actions, thereby voiding a possibly consented demand response (DR) strategy. The question on how accurate the controller model should be in order to obtain a performant optimal controller remains unanswered.

As reinforcement learning (RL) techniques are expected to bring an added value in systems subject to uncertainties, the hypothesis is that hybrid control methods, combining the strengths of MPC and RL, might lead to better control performance. A distinction can be made between (i) uncertainties that can be improved (though not fully eliminated) by adaptation through learning (e.g. model parameters, where the model remains an approximation of the real system), i.e. the parametric uncertainty (p) and (ii) uncertainties that cannot be improved (e.g. weather forecast) but that can be better dealt with in the decision-making process, i.e. the additive uncertainty (a). On-line learning/update of controller model parameters, objective function weights for conflicting objectives, controller hyperparameters, ... can improve the handling of uncertainties.

This PhD will focus on two main aspects: (i) explore and integrate different modelling paradigms (black, grey and white box) in the MPC framework to evaluate the impact of model accuracy on controller performance. Analysis of the sensitivity of MPC with respect to the different modelling paradigms will be performed. The key performance indicators required for this analysis will be defined in the course of the PhD; (ii) build on existing variants of MPC such as adaptive MPC, stochastic MPCap and RL- MPC to tackle the challenge of system uncertainties. The proposed solutions will be applied in optimal control with the aim of achieving a pre-set thermal comfort level in a building. The variants of MPC developed will be compared with recent multilevel (quasi-) Monte Carlo optimization algorithms (results of another PhD running in parallel).

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