Short-term load forecasting based on artificial neural networks parallel implementation

Abstract This paper presents the development and application of advanced neural networks to face successfully the problem of the short-term electric load forecasting. Several approaches including Gaussian encoding backpropagation (BP), window random activation, radial basis function networks, real-time recurrent neural networks and their innovative variations are proposed, compared and discussed in this paper. The performance of each presented structure is evaluated by means of an extensive simulation study, using actual hourly load data from the power system of the island of Crete, in Greece. The forecasting error statistical results, corresponding to the minimum and maximum load time-series, indicate that the load forecasting models proposed here provide significantly more accurate forecasts, compared to conventional autoregressive and BP forecasting models. Finally, a parallel processing approach for 24 h ahead forecasting is proposed and applied. According to this procedure, the requested load for each specific hour is forecasted, not only using the load time-series for this specific hour from the previous days, but also using the forecasted load data of the closer previous time steps for the same day. Thus, acceptable accuracy load predictions are obtained without the need of weather data that increase the system complexity, storage requirement and cost.

[1]  K. Jabbour,et al.  ALFA: automated load forecasting assistant , 1988 .

[2]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[3]  Dipti Srinivasan,et al.  Evolving artificial neural networks for short term load forecasting , 1998, Neurocomputing.

[4]  Sheng Chen,et al.  Regularized orthogonal least squares algorithm for constructing radial basis function networks , 1996 .

[5]  G. Irisarri,et al.  On-Line Load Forecasting for Energy Control Center Application , 1982, IEEE Transactions on Power Apparatus and Systems.

[6]  S. Vemuri,et al.  Neural network based short term load forecasting , 1993 .

[7]  Zhao Yang Dong,et al.  An adaptive neural-wavelet model for short term load forecasting , 2001 .

[8]  A. Mahmoud,et al.  Load Forecasting Bibliography Phase II , 1981, IEEE Transactions on Power Apparatus and Systems.

[9]  Saifur Rahman,et al.  An expert system based algorithm for short term load forecast , 1988 .

[10]  Yuan-Yih Hsu,et al.  Short term load forecasting of Taiwan power system using a knowledge-based expert system , 1990 .

[11]  George Stavrakakis,et al.  Wind power forecasting using advanced neural networks models , 1996 .

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

[13]  Chin-Teng Lin,et al.  Neural fuzzy systems , 1994 .

[14]  Saifur Rahman,et al.  Analysis and Evaluation of Five Short-Term Load Forecasting Techniques , 1989, IEEE Power Engineering Review.

[15]  S. J. Kiartzis,et al.  A neural network short term load forecasting model for the Greek power system , 1996 .

[16]  M. S. Kandil,et al.  Overview and comparison of long-term forecasting techniques for a fast developing utility: part I , 2001 .

[17]  Georges Kariniotakis,et al.  Load Forecasting Using Dynamic High-Orderr Neural Networks , 1993, Proceedings. Joint International Power Conference Athens Power Tech,.

[18]  Amir F. Atiya,et al.  An accelerated learning algorithm for multilayer perceptron networks , 1994, IEEE Trans. Neural Networks.

[19]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[20]  George Stavrakakis,et al.  Fatigue life prediction using a new moving window regression method , 1991 .

[21]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[22]  Shangyou Hao,et al.  An implementation of a neural network based load forecasting model for the EMS , 1994 .

[23]  Hubert A.B. Te Braake,et al.  Random activation weight neural net (RAWN) for fast non-iterative training. , 1995 .

[24]  Kwang Y. Lee,et al.  A study on neural networks for short-term load forecasting , 1991, Proceedings of the First International Forum on Applications of Neural Networks to Power Systems.