A hybrid deep learning algorithm for short-term electric load forecasting
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Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Over the last two decades, electric load forecasting has strengthened its significant role in electric power systems due to equalising the vital balance between generation and consumption of electrical energy for all actors of deregulated electricity markets. Artificial intelligence-based techniques are frequently used for short-term electric load forecasting owing to the abstruse nature of electric loads that can be influenced by a variety of factors. In this paper, a novel hybrid deep learning algorithm that combines GMDH and GRU networks is meticulously applied for one hour-ahead load forecasting of a large hospital complex. In the proposed algorithm, GMDH and GRU networks are employed for feature selection and prediction respectively. Consequently, the obtained results have demonstrated that the proposed algorithm is capable of reducing mean absolute percentage error by 12% and computational time by 5%.
Açıklama
29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORK
Anahtar Kelimeler
load forecasting, deep learning, feature selection, group method of data handling (GMDH), gated recurrent unit (GRU)
Kaynak
29th Ieee Conference on Signal Processing and Communications Applications (Siu 2021)
WoS Q Değeri
N/A
Scopus Q Değeri
0
