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Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks

Keles, Dogan, Scelle, Jonathan, Paraschiv, Florentina, Fichtner, Wolf
Applied energy 2016 v.162 pp. 218-230
algorithms, electricity, electricity costs, energy, neural networks, prices
Day-ahead electricity prices are generally used as reference prices for decisions done in energy trading, e.g. purchase and sale strategies are typically based on the day-ahead spot prices. Therefore, well-performing forecast methods for day-ahead electricity prices are essential for energy traders and supply companies.In this paper, a methodology based on artificial neuronal networks (ANN) is presented to forecast electricity prices. As the performance of an ANN forecast model depends on appropriate input parameter sets, the focus is set on the selection and preparation of fundamental data that has a noticeable impact on electricity prices. This is done with the help of different cluster algorithms, but also by comparing the results of the pre-selected model configurations in combination with different input parameter settings. After the determination of the optimal input parameters, affecting day-ahead electricity prices, and well-performing ANN configuration, the developed ANN model is applied for in-sample and out-of-sample analyses. The results show that the overall methodology leads to well-fitting electricity price forecasts, whereas forecast errors are as low as or even lower than other forecast models for electricity prices known from the literature.