Year 2017, Volume 5, Issue 2, Pages 329 - 338 2017-12-11

Application of Seasonal and Multivariable Grey Prediction Models for Short-Term Load Forecasting
Kısa Dönem Yük Tahmini için Mevsimsel ve Çok Değişkenli Gri Tahmin Modellerinin Uygulanması

Tuncay Özcan [1]

161 437

Short-term electricity load forecasting is one of the most important operations in electricity markets. The success in the operations of electricity market participants partially depends on the accuracy of load forecasts. In this paper, three grey prediction models, which are seasonal grey model (SGM), multivariable grey model (GM (1,N)) and genetic algorithm based multivariable grey model (GAGM (1,N)), are proposed for short-term load forecasting problem in day-ahead market. The effectiveness of these models is illustrated with two real-world data sets. Numerical results show that the genetic algorithm based multivariable grey model (GAGM (1,N)) is the most efficient grey forecasting model through its better forecast accuracy.
Kısa dönem elektrik yükü tahmini, elektrik piyasasında en önemli operasyonlardan biridir. Elektrik piyasasındaki işletmelerin operasyonlarındaki başarı, yük tahminlerinin doğruluğuna bağlıdır. Bu çalışmada, gün öncesi piyasasında kısa döneli yük tahmini problemi için mevsimsel gri model (SGM), çok değişkenli gri model (GM (1,N)) ve genetik algoritma esaslı gri model olmak üzere üç gri tahmin modeli önerilmiştir. Bu modellerin etkinliği, iki gerçek hayat veri kümesi ile gösterilmiştir. Sayısal sonuçlar, genetik algoritma esaslı gri modeli daha iyi tahmin doğruluğu sağlayarak en etkin gri tahmin modeli olduğunu göstermektedir.
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Subjects Social
Journal Section Articles

Orcid: 0000-0002-9520-2494
Author: Tuncay Özcan (Primary Author)
Institution: Istanbul University
Country: Turkey


Application Date: October 25, 2017
Acceptance Date: December 11, 2017
Publication Date: December 11, 2017

APA Özcan, T . (2017). Application of Seasonal and Multivariable Grey Prediction Models for Short-Term Load Forecasting. Alphanumeric Journal, 5 (2), 329-338. DOI: 10.17093/alphanumeric.359942