Artificial intelligence based hybrid structures for short-term load forecasting without temperature data

Esener I. I., YÜKSEL T., KURBAN M.

11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012, Boca Raton, FL, Amerika Birleşik Devletleri, 12 - 15 Aralık 2012, cilt.2, ss.457-462 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 2
  • Doi Numarası: 10.1109/icmla.2012.169
  • Basıldığı Şehir: Boca Raton, FL
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.457-462
  • Anahtar Kelimeler: artificial neural networks, empirical mode decomposition, radial basis function neural networks, short-term load forecasting, wavelet transform
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

Load forecasting is the first phase of electric power system planning for economic power generation-distribution, effective control and operation conditions of the system, and also energy pricing. In this study, short-term load forecasting, as the main tool for economic operation conditions, is realized. 24-hour-ahead load forecasting without temperature data for Turkey is aimed and structures with ANN, Wavelet Transform & ANN, Wavelet Transform & RBF Neural Network, and EMD & RBF Neural Network are proposed for forecasting process. Local holidays' load data is replaced with normal day's characteristic to remove the disturbing effects of those days. To have more accurate forecast, a regulation to load forecast is proposed. Unregulated and regulated forecast error percentages of all days except local holidays are calculated as average daily MAPE and maximum MAPE. All MAPE values are compared between the proposed structures. © 2012 IEEE.