  Recent advancements in the fields of artificial intelligence and machine
learning methods resulted in a significant increase of their popularity in the
literature, including electricity price forecasting. Said methods cover a very
broad spectrum, from decision trees, through random forests to various
artificial neural network models and hybrid approaches. In electricity price
forecasting, neural networks are the most popular machine learning method as
they provide a non-linear counterpart for well-tested linear regression models.
Their application, however, is not straightforward, with multiple
implementation factors to consider. One of such factors is the network's
structure. This paper provides a comprehensive comparison of two most common
structures when using the deep neural networks -- one that focuses on each hour
of the day separately, and one that reflects the daily auction structure and
models vectors of the prices. The results show a significant accuracy advantage
of using the latter, confirmed on data from five distinct power exchanges.
