Data Imputation for Electricity Price Forecasting in the Intraday Market

Öz

Trading volume of the Turkish Intraday Electricity Market has increased day by day. Due to this situation, it is critical for the market players to make accurate electricity price forecasts. The trading system in the intraday market is continuous trading. In this study, data is prepared to use it in the price forecasting by taking weighted average of the hourly prices. Missing value problem is encountered because of the hours without any transaction. To the best of our knowledge, there is not any study which deals with this problem in the electricity price forecasting literature. In this article, we focused on imputing values for missing data to use them in the electricity price forecasting. Missing value methods are tested, predictions are made by univariate lasso regression and the results are compared. Making predictions by imputing data has increased the performance in statistically significant terms. Our results showed that data imputation with day-ahead prices is the best method.

Anahtar Kelimeler

• : Electricity price forecasting
• Intraday Market
• Dayahead market
• Missing Data
• Lasso

Gün İçi Piyasası Elektrik Fiyat Tahmini için Eksik Verilerin Tamamlanması

Öz

Türkiye serbest elektrik piyasasında, gün içi piyasasının ticaret hacmi gün geçtikçe artmıştır. Bu durum piyasa katılımcıları için yüksek doğrulukta tahmin yapabilmeyi önemli hale getirmiştir. Gün içi piyasasında sürekli bir şekilde alışveriş yapılmaktadır. Bu çalışmada, öncelikle Türkiye gün içi elektrik fiyatlarının saatlik ağırlıklı ortalamaları alınarak, veri tahmin problemine hazır hale getirilmiştir. Piyasada işlem yapılmayan saatler bulunduğundan, eksik veri problemi ile karşılaşılmıştır. Araştırmalarımıza göre elektrik fiyat tahmini literatüründe bu problemin çözümüne yönelik bir çalışma bulunmamaktadır. Bu çalışmada fiyat tahmini için kullanılacak olan eğitim verilerindeki eksiklerin nasıl tamamlanacağı üzerinde durulmuştur. Eksik veri tamamlama yöntemleri denenmiş, tek değişkenli Lasso yöntemi ile tahminler yapılarak, sonuçları karşılaştırılmıştır. Eksik verileri tamamlayarak tahmin yapmanın sonuca istatistiksel olarak anlamlı şekilde katkısı olmuştur. Sonuçlarımız, eksik verileri gün öncesi piyasası değerleri ile tamamlamanın en başarılı yöntem olduğunu göstermiştir.

Anahtar Kelimeler

• Elektrik fiyat tahmini
• Gün içi piyasası
• Gün öncesi piyasası
• Eksik Veriler

Kaynakça

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