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In this work, we develop a novel data-driven model predictive controllerusing advanced techniques in the field of machine learning. The objective is toregulate control signals to adjust the desired internal room setpointtemperature, affected indirectly by the external weather states. Themethodology involves developing a time-series machine learning model witheither a Long Short Term Memory model (LSTM) or a Gradient Boosting Algorithm(XGboost), capable of forecasting this weather states for any desired timehorizon and concurrently optimising the control signals to the desired setpoint. The supervised learning model for mapping the weather states togetherwith the control signals to the room temperature is constructed using apreviously developed methodology called Cluster Classify regress (CCR), whichis similar in style but scales better to high dimensional dataset than thewell-known Mixture-of-Experts. The overall method called CCR-MPC involves acombination of a time series model for weather states prediction, CCR forforwarding and any numerical optimisation method for solving the inverseproblem. Forward uncertainty quantification (Forward-UQ) leans towards theregression model in the CCR and is attainable using a Bayesian deep neuralnetwork or a Gaussian process (GP). For this work, in the CCR modulation, weemploy K-means clustering for Clustering, XGboost classifier for Classificationand 5th order polynomial regression for Regression. Inverse UQ can also beobtained by using an I-ES approach for solving the inverse problem or even thewell-known Markov chain Monte Carlo (MCMC) approach. The developed CCR-MPC iselegant, and as seen on the numerical experiments is able to optimise thecontroller to attain the desired setpoint temperature.

A Novel Cluster Classify Regress Model Predictive Controller Formulation; CCR-MPC