  An ensuing challenge in Artificial Intelligence (AI) is the perceived
difficulty in interpreting sophisticated machine learning models, whose
ever-increasing complexity makes it hard for such models to be understood,
trusted and thus accepted by human beings. The lack, if not complete absence,
of interpretability for these so-called black-box models can lead to serious
economic and ethical consequences, thereby hindering the development and
deployment of AI in wider fields, particularly in those involving critical and
regulatory applications. Yet, the building services industry is a
highly-regulated domain requiring transparency and decision-making processes
that can be understood and trusted by humans. To this end, the design and
implementation of autonomous Heating, Ventilation and Air Conditioning systems
for the automatic but concurrently interpretable optimisation of energy
efficiency and room thermal comfort is of topical interest. This work therefore
presents an interpretable machine learning model aimed at predicting room
temperature in non-domestic buildings, for the purpose of optimising the use of
the installed HVAC system. We demonstrate experimentally that the proposed
model can accurately forecast room temperatures eight hours ahead in real-time
by taking into account historical RT information, as well as additional
environmental and time-series features. In this paper, an enhanced feature
engineering process is conducted based on the Exploratory Data Analysis
results. Furthermore, beyond the commonly used Interpretable Machine Learning
techniques, we propose a Permutation Feature-based Frequency Response Analysis
(PF-FRA) method for quantifying the contributions of the different predictors
in the frequency domain. Based on the generated reason codes, we find that the
historical RT feature is the dominant factor that has most impact on the model
prediction.

Machine Learning;Artificial Intelligence