A Machine-Learning Based Approach for Data- Driven Identification of Heating Dynamics of Buildings’ Living-Spaces

— Modeling the heating dynamics of a given
living-space of a real building remains a challenging
engineering-science problem because of the quite large
number of diverse kinds of involved parameters and their
usually nonlinear interdependency. However, the need of
such living-spaces’ heating dynamics modeling appears as a foremost requirement for designing adaptive controllers
scheming the complex behaviors of nowadays’ smart
buildings. In this context and through considering the
above-mentioned complex dynamics’ modeling within the
slant of “Time-Series Prediction” paradigm, in this paper
we propose a Machine-Learning-based data-driven
approach for overcoming difficulties inherent to the
aforementioned challenging engineering-science problem.
The proposed approach takes advantage from the nonlinear
autoregressive exogenous (NARX) model’s capabilities in
time-series’ forecasting and the Multi-Layer Perceptron’s
(MLP) learning and generalization skills. The proposed
approach has been applied for living-space heating
dynamics identification of a fully automated four-floor real
building located at Senart Campus of University Paris-Est
Créteil (UPEC). The obtained results assessing the accuracy
of the investigated Machine-Learning-based approach are
reported and discussed.