Application of Seasonal and Multivariable Grey Prediction Models for Short-Term Load Forecasting
Tuncay Özcan, Ph.D.
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.
Keywords: Genetic Algorithm, Grey Prediction, Parameter Optimization, Short Term Load Forecasting
Jel Classification: C44, C53, C63
Kısa Dönem Yük Tahmini için Mevsimsel ve Çok Değişkenli Gri Tahmin Modellerinin Uygulanması
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.
Anahtar Kelimeler: Genetik Algoritma, Gri Tahmin, Kısa Dönem Yük Tahmini, Parametre Optimizasyonu
Cite this article
Özcan, T. (2017). Application of Seasonal and Multivariable Grey Prediction Models for Short-Term Load Forecasting. Alphanumeric Journal, 5(2), 329-338. http://dx.doi.org/10.17093/alphanumeric.359942
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Volume 5, Issue 2, 2017
Received: Oct. 25, 2017
Accepted: Dec. 11, 2017
Published: Dec. 11, 2017
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