  This paper presents an approach based on higher order dynamic mode
decomposition (HODMD) to model, analyse, and forecast energy behaviour in an
urban agriculture farm situated in a retrofitted London underground tunnel,
where observed measurements are influenced by noisy and occasionally transient
conditions. HODMD is a data-driven reduced order modelling method typically
used to analyse and predict highly noisy and complex flows in fluid dynamics or
any type of complex data from dynamical systems. HODMD is a recent extension of
the classical dynamic mode decomposition method (DMD), customised to handle
scenarios where the spectral complexity underlying the measurement data is
higher than its spatial complexity, such as is the environmental behaviour of
the farm. HODMD decomposes temporal data as a linear expansion of
physically-meaningful DMD-modes in a semi-automatic approach, using a
time-delay embedded approach. We apply HODMD to three seasonal scenarios using
real data measured by sensors located at at the cross-sectional centre of the
the underground farm. Through the study we revealed three
physically-interpretable mode pairs that govern the environmental behaviour at
the centre of the farm, consistently across environmental scenarios.
Subsequently, we demonstrate how we can reconstruct the fundamental structure
of the observed time-series using only these modes, and forecast for three days
ahead, as one, compact and interpretable reduced-order model. We find HODMD to
serve as a robust, semi-automatic modelling alternative for predictive
modelling in Digital Twins.
