Marinescu_2014_hybrid.pdf (228.83 kB)
A hybrid approach to very small scale electrical demand forecasting
journal contribution
posted on 2014-06-24, 10:36 authored by Andrei Marinescu, Colin Harris, Ivana Dusparic, Vinny Cahill, Siobhán ClarkeMicrogrid management and scheduling can considerably
benefit from day-ahead demand forecasting. Until now,
most of the research in the field of electrical demand forecasting
has been done on large-scale systems, such as national or
municipal level grids. This paper examines a hybrid method that
attempts to accurately estimate day-ahead electrical demand of
a small community of houses resembling the load of a single
transformer, the equivalent sizing of a small virtual power plant
or microgrid. We have combined the advantages of several
forecasting methods into a novel hybrid approach: artificial
neural networks, fuzzy logic, auto-regressive moving average and
wavelet smoothing. The combined system has been tested over two
different scenarios, comprising communities of 90 houses and 230
houses, sampled from a smart-meter field trial in Ireland. Our
hybrid approach achieves results of 3.22% NRMSE and 2.39%
NRMSE respectively, leading to general improvements of 11%-
28% when compared to the individual methods.
History
Publication
IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT);pp. 1-5Publisher
IEEE Computer SocietyNote
peer-reviewedOther Funding information
SFIRights
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EnglishExternal identifier
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