Sammendrag
This review article investigates the methods proposed for disaggregating
the space heating units’ load from the aggregate electricity load of commercial
and residential buildings. It explores conventional approaches together with those
that employ traditional machine learning, deep supervised learning and reinforcement
learning. The review also outlines corresponding data requirements and
examines the suitability of a commonly utilised toolkit for disaggregating heating
loads from low-frequency aggregate power measurements. It is shown that most
of the proposed approaches have been applied to high-resolution measurements
and that few studies have been dedicated to low-resolution aggregate loads (e.g.
provided by smart meters). Furthermore, only a few methods have taken account
of special considerations for heating technologies, given the corresponding governing
physical phenomena. Accordingly, the recommendations for future works
include adding a rigorous pre-processing step, in which features inspired by the
building physics (e.g. lagged values for the ambient conditions and values that
represent the correlation between heating consumption and outdoor temperature)
are added to the available input feature pool. Such a pipeline may benefit from
deep supervised learning or reinforcement learning methods, as these methods
are shown to offer higher performance compared to traditional machine learning
algorithms for load disaggregation.
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