  Highly accurate interval forecasting of electricity demand is fundamental to
the success of reducing the risk when making power system planning and
operational decisions by providing a range rather than point estimation. In
this study, a novel modeling framework integrating bivariate empirical mode
decomposition (BEMD) and support vector regression (SVR), extended from the
well-established empirical mode decomposition (EMD) based time series modeling
framework in the energy demand forecasting literature, is proposed for interval
forecasting of electricity demand. The novelty of this study arises from the
employment of BEMD, a new extension of classical empirical model decomposition
(EMD) destined to handle bivariate time series treated as complex-valued time
series, as decomposition method instead of classical EMD only capable of
decomposing one-dimensional single-valued time series. This proposed modeling
framework is endowed with BEMD to decompose simultaneously both the lower and
upper bounds time series, constructed in forms of complex-valued time series,
of electricity demand on a monthly per hour basis, resulting in capturing the
potential interrelationship between lower and upper bounds. The proposed
modeling framework is justified with monthly interval-valued electricity demand
data per hour in Pennsylvania-New Jersey-Maryland Interconnection, indicating
it as a promising method for interval-valued electricity demand forecasting.
