Long-term Electrical Load Forecasting based on Economic and Demographic Data for Turkey
Küçük Resim Yok
Tarih
2013
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Load forecasting is very important to operate the electric power systems. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Long term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption data, national incoming, urbanization rate, population increasing rate and as well as other economic parameters. Artificial Neural Network (ANN) and Artificial Neural Fuzzy Inference System (ANFIS) are the famous artificial intelligence methods and have widely used to solve forecasting problems in literature. In this study, artificial intelligence methods and mathematical modeling (MM) are used to forecast long term energy consumption and peak load for Turkey. The four different input data are used to obtain two different outputs in all three methods. Using the four different variables especially in mathematical modeling has been a novelty for Turkey case study. The results obtained from ANFIS, ANN and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and mean absolute error (MAE) are used.
Açıklama
14th IEEE International Symposium on Computational Intelligence and Informatics (CINTI) -- NOV 19-21, 2013 -- Budapest, HUNGARY
Anahtar Kelimeler
Kaynak
14TH IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND INFORMATICS (CINTI)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
