Short-term load forecasting without meteorological data using AI-based structures

Esener I. I., YÜKSEL T., KURBAN M.

Turkish Journal of Electrical Engineering and Computer Sciences, cilt.23, sa.2, ss.370-380, 2015 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23 Sayı: 2
  • Basım Tarihi: 2015
  • Doi Numarası: 10.3906/elk-1209-28
  • Dergi Adı: Turkish Journal of Electrical Engineering and Computer Sciences
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.370-380
  • 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

© TUBITAK.STLF is used in making decisions about economical power generation capacity, fuel purchasing, safety assessment, and power system planning in order to have economical power conditions. In this study, Turkey's 24-hourahead load forecasting without meteorological data is studied. ANN, wavelet transform and ANN, wavelet transform and RBF NN, and EMD and RBF NN structures are used in STLF procedures. Local holidays' historical load data are changed into data with normal day characteristics, and the estimation results of these days are not included in error computation. To obtain more accurate results, a regulation on forecasted loads is proposed. Regulated and unregulated forecasting error percentages are computed as daily average MAPE and maximum daily MAPE, and compared between the proposed structures. A simulation is performed for the years 2009-2010 via the user interface created using MATLAB GUI.