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Deep Learning Approach to Power Demand Forecasting in Polish Power System
Content Provider | MDPI |
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Author | Ciechulski, Tomasz Osowski, Stanisław |
Copyright Year | 2020 |
Description | 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. |
Starting Page | 6154 |
e-ISSN | 19961073 |
DOI | 10.3390/en13226154 |
Journal | Energies |
Issue Number | 22 |
Volume Number | 13 |
Language | English |
Publisher | MDPI |
Publisher Date | 2020-11-23 |
Access Restriction | Open |
Subject Keyword | Energies Industrial Engineering Power Demand Forecasting Diagnostic Features Neural Networks Deep Learning |
Content Type | Text |
Resource Type | Article |