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    DEEP LEARNING APPROACH TO FORECASTING ELECTRICITY PRICE FROM LOAD DATA
    (2020) Babushkin, Vladimir; Căpățână, Gheorghe
    The accurate forecasting of electricity price and load is essential for maintaining a stable interplay between demand and supply in the dynamic electricity market. In this work we propose a deep Convolutional Neural Network-based model for day-ahead electricity price forecasting from historical price/load data and predicted load values. The model was tested on the data for New York and New South Wales and demonstrated high prediction accuracy for both datasets.