Short term and medium term power distribution load forecasting by neural networks

Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey.

[1]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[2]  Philippe De Wilde.  Neural Network Models: An Analysis , 1995 .

[3]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[4]  M. K. Soni,et al.  Artificial neural network based peak load forecasting using Levenberg-Marquardt and quasi-Newton methods , 2002 .

[5]  H. Stoll.  Least-Cost Electric Utility Planning , 1989 .

[6]  Chia-Yon Chen,et al.  Regional load forecasting in Taiwanapplications of artificial neural networks , 2003 .

[7]  Kostas S. Metaxiotis,et al.  Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher , 2003 .

[8]  K Padmakumari,et al.  Long term distribution demand forecasting using neuro fuzzy computations , 1999 .

[9]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[10]  Tharam S. Dillon,et al.  Short term load forecasting using an adaptive neural network , 1991 .

[11]  G. J. Rogers,et al.  Modeling effects of system frequency variations in induction motor dynamics using singular perturbations , 2000 .

[12]  J. D. McDonald,et al.  A real-time implementation of short-term load forecasting for distribution power systems , 1994 .

[13]  A. D. Kulkarni,et al.  A neuro-expert system for planning and load forecasting of distribution systems , 1999 .

[14]  H Lee Willis,et al.  Power distribution planning reference book , 2000 .

[15]  Saifur Rahman,et al.  Short-term load forecasting with local ANN predictors , 1999 .

[16]  George G. Karady,et al.  Advancement in the application of neural networks for short-term load forecasting , 1992 .

[17]  S. A. Villalba,et al.  Hybrid demand model for load estimation and short term load forecasting in distribution electric systems , 2000 .

[18]  Saifur Rahman,et al.  Input variable selection for ANN-based short-term load forecasting , 1998 .

[19]  Chia-Yon Chen,et al.  Applications of improved grey prediction model for power demand forecasting , 2003 .

[20]  Tomonobu Senjyu,et al.  Next Day Peak Load Forecasting Using Neural Network With Adaptive Learning Algorithm Based On Similarity , 2000 .