Short term and medium term power distribution load forecasting by neural networks
Küçük Resim Yok
Tarih
2005
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
PERGAMON-ELSEVIER SCIENCE LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey. (C) 2004 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
neural networks, short term, medium term, peak, load forecasting, power distribution systems
Kaynak
ENERGY CONVERSION AND MANAGEMENT
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
46
Sayı
45574
