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Electricity prices strongly depend on seasonality of different time scales,therefore any forecasting of electricity prices has to account for it. Neuralnetworks have proven successful in short-term price-forecasting, butcomplicated architectures like LSTM are used to integrate the seasonalbehaviour. This paper shows that simple neural network architectures like DNNswith an embedding layer for seasonality information can generate a competitiveforecast. The embedding-based processing of calendar information additionallyopens up new applications for neural networks in electricity trading, such asthe generation of price forward curves. Besides the theoretical foundation,this paper also provides an empirical multi-year study on the Germanelectricity market for both applications and derives economical insights fromthe embedding layer. The study shows that in short-term price-forecasting themean absolute error of the proposed neural networks with an embedding layer isbetter than the LSTM and time-series benchmark models and even slightly betteras our best benchmark model with a sophisticated hyperparameter optimization.The results are supported by a statistical analysis using Friedman and Holm'stests.

Short- and long-term forecasting of electricity prices using embedding of calendar information in neural networks