Determining relevant features in estimating short-term power load of a small house via feature selection by extreme learning machine
Yükleniyor...
Dosyalar
Tarih
2017-11-02
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Attribution-NonCommercial-ShareAlike 3.0 United States
Attribution-NonCommercial-ShareAlike 3.0 United States
Özet
Estimating short-term power load is a fundamental issue in the power distribution system. Since short-term power load is related to many parameters such as weather conditions, and time. The aim of this study is to determine the relevant parameters in estimating short-term power load not only in order to decrease the computational cost, but also to achieve higher success rates. Furthermore, by using selected features the required memory, equipment and communication costs are also decreased in real time applications. Feature selection by extreme learning machine method was used in determining relevant features. The short-term power loads of two houses (one of them has a power generation capability) were used in tests and achieved results showed lower error rates were obtained by using less number of features.
Açıklama
Anahtar Kelimeler
Extreme Learning Machine, Feature Selection, Short-Term Power Load
Kaynak
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Ertuğrul, Ö F., Sezgin, N., Öztekin, A., Tağluk, M. E. (2017). Determining relevant features in estimating short-term power load of a small house via feature selection by extreme learning machine. 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 16-17 Sept. 2017, Malatya, Turkey. https://doi.org/10.1109/idap.2017.8090345
