In this work, we develop a novel data-driven model predictive controller using advanced techniques in the field of machine learning. The objective is to regulate control signals to adjust the desired internal room setpoint temperature, affected indirectly by the external weather states. The methodology involves developing a time-series machine learning model with either a Long Short Term Memory model (LSTM) or a Gradient Boosting Algorithm (XGboost), capable of forecasting this weather states for any desired time horizon and concurrently optimising the control signals to the desired set point. The supervised learning model for mapping the weather states together with the control signals to the room temperature is constructed using a previously developed methodology called Cluster Classify regress (CCR), which is similar in style but scales better to high dimensional dataset than the well-known Mixture-of-Experts. The overall method called CCR-MPC involves a combination of a time series model for weather states prediction, CCR for forwarding and any numerical optimisation method for solving the inverse problem. Forward uncertainty quantification (Forward-UQ) leans towards the regression model in the CCR and is attainable using a Bayesian deep neural network or a Gaussian process (GP). For this work, in the CCR modulation, we employ K-means clustering for Clustering, XGboost classifier for Classification and 5th order polynomial regression for Regression. Inverse UQ can also be obtained by using an I-ES approach for solving the inverse problem or even the well-known Markov chain Monte Carlo (MCMC) approach. The developed CCR-MPC is elegant, and as seen on the numerical experiments is able to optimise the controller to attain the desired setpoint temperature.
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