A benchmark of GRU and LSTM networks for short-term electric load forecasting
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Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Recently, electric power systems have been modernised to be integrated with distributed energy systems having intermittent characteristics. Herein, short-term electric load forecasting (STLF), which covers hour, day, or week-ahead predictions of electric loads, is a crucial piece of the modern power system puzzle whose level of complexity has become more and more sophisticated owing to incorporating microgrids and smart grids. Due to the nonlinear feature of electric loads and the uncertainties in the modern power systems, deep learning algorithms are frequently applied to STLF problem which can be described as an arduous challenge because of being affected by several impacts. In this paper, gated recurrent unit (GRU) and long short-term memory (LSTM) networks are implemented in forecasting an hour-ahead electric loads of a large hospital complex located in Adana, Turkey. Overall results belonging to the benchmark of GRU and LSTM networks for STLF revealed that employing GRU networks performed better in terms of mean absolute percentage error (MAPE) by 7.8% and computational time by 15.5% in comparison with utilising LSTM networks. © 2021 IEEE.
Açıklama
2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021 -- 29 September 2021 through 30 September 2021 -- Virtual, Online -- 173514
Anahtar Kelimeler
comparison, deep learning (DL), gated recurrent unit (GRU), long short-term memory (LSTM), Short-term electric load forecasting (STLF)
Kaynak
2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021
