A Short-Term Load Demand Forecasting based on the Method of LSTM
Yükleniyor...
Dosyalar
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Electricity energy is produced from another energy source like fossil source such as oil, coil, natural gas renewable energy sources such as hydraulic, wind, solar. Their storage in high amount is a problematic issue. Therefore, the balance between the power generation and demanded power must be satisfied at all times. This is an obligation especially for companies that generate, transmit and distribute electrical energy. In this paper, a short term load demand forecasting based on a long short term memory (LSTM) is addressed, which may help planning operators for Turkish electricity market. The results of advocated approach were compared by the ones based on recurrent neural network As a result, it is found that the proposed LSTM approach can predict especially daily and weekly demands with an accuracy more than 90%.
Açıklama
10th IEEE International Conference on Renewable Energy Research and Applications (ICRERA) -- SEP 26-29, 2021 -- Istanbul, TURKEY
Anahtar Kelimeler
Short Term Load Forecasting; Long-Short-Term Memory; Recurrent Neural Network
Kaynak
10th Ieee International Conference on Renewable Energy Research and Applications (Icrera 2021)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
