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Deep Learning Based Electricity Demand Forecasting in Different Domains

Credit to Download: 1 | Page Numbers 7 | Abstract Views: 34
Year: 2020
COI code: JR_IJEE-11-1_006
Paper Language: English

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Authors Deep Learning Based Electricity Demand Forecasting in Different Domains

  M. Imani - Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

Abstract:

Electricity demand forecasting is an important task in power grids. Most of researches on electrical load forecasting have been done in the time domain. But, the electrical time series has a non-stationary inherence that makes hard load prediction. Moreover, valuable information is hidden in the electrical load sequence which is not open in the time domain. To deal with these difficulties, a new electricity demand forecasting framework is proposed in this work. In the proposed framework, at first, a new feature space of electrical load sequence is composed. The provided domain involves complementary information about shape and variations of electrical load sequence. Then, the obtained load features are integrated with the original load values in time domain to allow a rich input for predictor. Finally, a powerful deep learning technique from the family of recurrent neural networks, named long-short term memory, is used to learn electricity demand from the provided features in single and hybrid domains. The following domains are investigated in this work: frequency, cepstrum, spectral centroid, spectral roll-off, spectral flux, energy, time difference, frequency difference, Gabor and collaborative representation. The experiments show that the use of time difference domain decreases the mean absolute percent error from 0.0332 to 0.0056.

Keywords:

Frequency Domain, Load forecasting, Long-short Term Memory, Time Domain

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COI code: JR_IJEE-11-1_006

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Imani, M., 2020, Deep Learning Based Electricity Demand Forecasting in Different Domains, Iranica Journal of Energy and Environment (IJEE) 11 (1), https://www.civilica.com/Paper-JR_IJEE-JR_IJEE-11-1_006.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
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Type: state university
Paper No.: 25732
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