MULTI-STEP FORWARD FORECASTING OF ELECTRICAL POWER GENERATION IN LIGNITE-FIRED THERMAL POWER PLANT

Öz

This paper presents multi-step forward forecasting studies using real-time generated electrical power time series. Nonlinear Automatic Regression (NAR) and Autoregressive Integrated Moving Average (ARIMA) models were created and applied to the generator power time series produced in Afşin-Elbistan Thermal B Plant. The data were divided into three categories as raw, 10-moving average and 20-moving average while the number of forwarding steps has been established as 6-step forward, 12-step forward and 20-step forward. Performance results of NAR and ARIMA models were presented with 6 scenarios, and then, the results were compared with tables and graphs. As a result of all studies, it has been observed that the model’s success was greatly affected by moving average and forward steps parameters.

Anahtar Kelimeler

• ARIMA
• NAR
• Artificial Neural Network
• Time Series
• Multi-Step Forward Forecast

LİNYİT YAKITLI TERMİK SANTRALDE ELEKTRİK ENERJİSİ ÜRETİMİNİN ÇOK ADIMLI İLERİ TAHMİNİ

Öz

Bu çalışmada gerçek zamanlı üretilen elektriksel güç zaman serilerinin kullanılmasıyla çok adımlı ileriye dönük tahmin çalışmaları anlatılmaktadır. Doğrusal Olmayan Otoregresif (NAR) ve Otoregresif Hareketli Ortalama (ARIMA) modelleri oluşturulmuş ve Afşin-Elbistan Termik B Santralinde üretilen generatör güç zaman serilerine uygulanmıştır. Veriler ham, 10 hareketli ortalama ve 20 hareketli ortalama olarak üç kategoriye ayrılırken, adım sayısı 6 adım ileri, 12 adım ileri ve 20 adım ileri olarak belirlenmiştir. NAR ve ARIMA modellerinin performans sonuçları 6 senaryo ile oluşturulmuş, ardından sonuçlar tablo ve grafikler ile karşılaştırılmıştır. Tüm çalışmalar sonucunda, hareketli ortalama ve ileri adım sayısı parametrelerinin model başarısını büyük ölçüde etkilediği görülmüştür.

Anahtar Kelimeler

• ARIMA
• NAR
• Yapay Sinir Ağları
• Zaman Serisi
• Çok-Adımlı İleri Tahmin

Kaynakça

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