Türk Elektrik Piyasalarında YSA Yaklaşımıyla Elektrik Yükü Tahmini

Year 2020, , 170 - 184, 31.12.2020

Fazıl Gökgöz , Fahrettin Filiz

https://doi.org/10.33721/by.834285

Cited By: 1

Abstract

Elektrik yük tahmini yapabilmek, elektrik hizmetleri, enerji santralleri ve düzenleyiciler için gereklidir. Enerji politikalarını için büyük öneme sahip olan elektrik yükü tahminlerinin sağlıklı ve güvenilir sonuçlar üretmesi esastır. Yapay sinir ağları (YSA) karmaşık ve doğrusal olmayan ilişkileri öğrenebilir. Bu makale, elektrik yükü tahmini için 400 farklı YSA modelini tanımlamaktadır. Model performansları Ortalama Mutlak Yüzde Hata (MAPE) ve Diebold-Mariano (DM) testi ile karşılaştırılmıştır. Bu çalışma için kullanılan elektrik yükü verileri 2014 ile 2016 yılları arasında değişmektedir. Farklı modeller için YSA'nın tahmin kabiliyetleri tartışılmıştır. Log-sigmoid aktarım işlevine sahip Levenberg-Marquardt (LM), en iyi performanslı YSA modelini eğitir.

Keywords

Elektrik Yük Tahmini, Yapay Sinir Ağları, Türkiye

Electricity Load Forecasting via ANN Approach in Turkish Electricity Markets

Abstract

Forecasting electricity load has become the essential task for electric utilities, power plants and regulators. It is essential that electricity load forecasts, which are a vital necessity of energy policies, produce healthy and reliable results. Artificial neural networks (ANN) can learn complex and nonlinear relationships. This article introduces 400 different ANN models for electricity load forecasting. Model performances have compared with Mean Absolute Percentage Error (MAPE) and Diebold-Mariano (DM) test. The electricity load data used for this study range from 2014 to 2016. The variation in forecasting ability of ANN for different models has also discussed. Levenberg-Marquardt (LM) with log-sigmoid transfer function trains the best performance ANN model.

Keywords

Electricity Load Forecast, Artificial Neural Network, Turkey

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