Deep Learning Approach to Power Demand Forecasting in Polish Power System

• Published in: Energies (Basel) Vol. 13; no. 22; p. 6154
• Main Authors: Ciechulski, Tomasz, Osowski, Stanisław
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.11.2020
• Subjects: deep learning; diagnostic features; neural networks; power demand forecasting
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13226154

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 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.