UDG researchers will be presenting the paper titled “Forecasting Day-Ahead Electricity Price with Artificial Neural Networks: a Comparison of Architectures”, authored by M. Pavicevic and T. Popovic, at the upcoming IEEE IDAACS 2021 conference. The conference will be taking place on 22-25 September 2021. The paper presents the use of AI/ML algorithms and CNN architectures for prediction of prices for the electric energy markets. More info on the conference is available at the following link.
ABSTRACT – The spot price prediction for the electric energy markets is a widely approached problem, used by many participants in the market. The ever-shifting rules and regulations, rising percentage of the electricity on the market being produced by solar and wind plants and many stochastic factors influencing it make the market price of electricity very volatile and hard to forecast. Many methods are used to tackle this problem, and their efficiency varies from dataset to dataset. In this work, we use the dataset of hourly day-ahead spot prices from the Hungarian HUPX market, and couple it with weather data for Hungary. We test various types of Dense, Recurrent and Convolutional neural network architectures and report on the results.
