An adaptive standardisation model for Day-Ahead electricity price
  forecasting
Carlos Sebastián Carlos E. González-Guillén Jesús Juan
The study of Day-Ahead prices in the electricity market is one of the most
popular problems in time series forecasting. Previous research has focused on
employing increasingly complex learning algorithms to capture the sophisticated
dynamics of the market. However, there is a threshold where increased
complexity fails to yield substantial improvements. In this work, we propose an
alternative approach by introducing an adaptive standardisation to mitigate the
effects of dataset shifts that commonly occur in the market. By doing so,
learning algorithms can prioritize uncovering the true relationship between the
target variable and the explanatory variables. We investigate four distinct
markets, including two novel datasets, previously unexplored in the literature.
These datasets provide a more realistic representation of the current market
context, that conventional datasets do not show. The results demonstrate a
significant improvement across all four markets, using learning algorithms that
are less complex yet widely accepted in the literature. This significant
advancement unveils opens up new lines of research in this field, highlighting
the potential of adaptive transformations in enhancing the performance of
forecasting models.