  In the context of smart grids and load balancing, daily peak load forecasting
has become a critical activity for stakeholders of the energy industry. An
understanding of peak magnitude and timing is paramount for the implementation
of smart grid strategies such as peak shaving. The modelling approach proposed
in this paper leverages high-resolution and low-resolution information to
forecast daily peak demand size and timing. The resulting multi-resolution
modelling framework can be adapted to different model classes. The key
contributions of this paper are a) a general and formal introduction to the
multi-resolution modelling approach, b) a discussion on modelling approaches at
different resolutions implemented via Generalised Additive Models and Neural
Networks and c) experimental results on real data from the UK electricity
market. The results confirm that the predictive performance of the proposed
modelling approach is competitive with that of low- and high-resolution
alternatives.

Machine Learning;Methodology