Deep Learning Approach to Power Demand Forecasting in Polish Power System

Tomasz Ciechulski, Stanisław Osowski
2020 Energies  
The paper presents a new approach to predicting the 24-h electricity power demand in the Polish Power System (PPS, or Krajowy System Elektroenergetyczny—KSE) using the deep learning approach. The prediction system uses a deep multilayer autoencoder to generate diagnostic features and an ensemble of two neural networks: multilayer perceptron and radial basis function network and support vector machine in regression model, for final 24-h forecast one-week advance. The period of the data that is
more » ... the data that is the subject of the experiments is 2014–2019, which has been divided into two parts: Learning data (2014–2018), and test data (2019). The numerical experiments have shown the advantage of deep learning over classical approaches of neural networks for the problem of power demand prediction.
doi:10.3390/en13226154 fatcat:n4uzktl7dfdjpcblzjmq5a5kle