  Accurate day-ahead peak load forecasting is crucial not only for power
dispatching but also has a great interest to investors and energy policy maker
as well as government. Literature reveals that 1% error drop of forecast can
reduce 10 million pounds operational cost. Thus, this study proposed a novel
hybrid predictive model built upon multivariate empirical mode decomposition
(MEMD) and support vector regression (SVR) with parameters optimized by
particle swarm optimization (PSO), which is able to capture precise electricity
peak load. The novelty of this study mainly comes from the application of MEMD,
which enables the multivariate data decomposition to effectively extract
inherent information among relevant variables at different time frequency
during the deterioration of multivariate over time. Two real-world load data
sets from the New South Wales (NSW) and the Victoria (VIC) in Australia have
been considered to verify the superiority of the proposed MEMD-PSO-SVR hybrid
model. The quantitative and comprehensive assessments are performed, and the
results indicate that the proposed MEMD-PSO-SVR method is a promising
alternative for day-ahead electricity peak load forecasting.
