Deep Learning Electric Load Forecasting for the Greek Power System

Long Short-Term Memory and Gated Recurrent Unit-based networks are examined for short-term load forecasting for the Greek power system, where predictions of the next hour's load are made for the whole year. The recurrent property of the forecasters has the advantage to allow the use of a reduced input set since no prior knowledge of the most suitable previous loads is required for the model to identify the temporal relations that exist in the load time-series. An extensive simulation analysis is conducted, to evaluate the forecasters' performance and to highlight their particular learning characteristics. Both deep learning-based forecasters perform very efficiently, with LSTM achieving an average percentage error of 1.18% and a root mean squared error of 83.8 MW, while GRU performs similarly with respective values of 1.17% and 83.4 MW.