  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.
