SFB649DP2014 030
Forecasting Generalized Quantiles of Electricity
Demand: A Functional Data Approach
Brenda López Cabrera
Franziska Schulz
Abstract:
Electricity load forecasts are an integral part of many decision-making processes
in the electricity market. However, most literature on electricity load
forecasting concentrates on deterministic forecasts, neglecting possibly important
information about uncertainty. A more complete picture of future demand
can be obtained by using distributional forecasts, allowing for a more efficient
decision-making. A predictive density can be fully characterized by tail measures
such as quantiles and expectiles. Furthermore, interest often lies in the
accurate estimation of tail events rather than in the mean or median. We propose
a new methodology to obtain probabilistic forecasts of electricity load,
that is based on functional data analysis of generalized quantile curves. The
core of the methodology is dimension reduction based on functional principal
components of tail curves with dependence structure. The approach has several
advantages, such as flexible inclusion of explanatory variables including
meteorological forecasts and no distributional assumptions. The methodology
is applied to load data from a transmission system operator (TSO) and
a balancing unit in Germany. Our forecast method is evaluated against other
models including the TSO forecast model. It outperforms them in terms of
mean absolute percentage error (MAPE) and achieves a MAPE of 2:7% for
the TSO.
Keywords:
Electricity, Load forecasting, FPCA
JEL Classification:
G19, G29, G22, Q14, Q49, Q59