Neural and mathematical modeling approaches for hourly long term loadforecasting

Filik Ü. B., Gerek Ö. N., Kurban M.

ICIC Express Letters, cilt.3, sa.4, ss.1125-1130, 2009 (Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 3 Sayı: 4
  • Basım Tarihi: 2009
  • Dergi Adı: ICIC Express Letters
  • Derginin Tarandığı İndeksler: Scopus
  • Sayfa Sayıları: ss.1125-1130
  • Anahtar Kelimeler: Artificial neural network, Load forecasting, Mathematical modeling
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Hayır

Özet

In this work, a mathematical model and an Artificial Neural Network (ANN)approach are constructed for the hourly forecasting of long term electric energydemand. Unlike former studies, these methods produce long term load forecastingresults at an accuracy level of hourly precision. The proposed mathematicalmodel of the load is compared with a feed-forward ANN model output in the senseof Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Themathematical model provides a simple, intuitive and more generalized form,whereas the ANN model provides a specified model fine-tuned for the availabledata. The suitability of these methods is illustrated and verified using4-year-long real-life hourly load data taken from Turkish Electric PowerCompany. ICIC International © 2009.