Short-term load forecasting in large scale electrical utility using artificial neural network

This paper presents a novel method for short-term load forecasting (STLF), based on artificial neural network (ANN), targeted for use in large-scale systems such as distribution management system (DMS). The system comprises of a preprocessing unit (PPU) and a feed forward ANN ordered in a sequence. PPU prepares the data and feeds them as input to the ANN, which calculates the hourly load forecasts. Preprocessing of the entering data reduces the size of the input space to the ANN, which improves the generalization capability and shortens the training time of the network. Reduced dimension of the input space also diminishes the number of parameters to be set in a training procedure, allowing smaller training set, and thus online usage and adaptation. This is important for a real-world power system where a sufficient set of historical data (training points) may not always be available, for different reasons. Ease of use and fast adaptation are necessary when predictions need to carry out in a large number of nodes in the power grid. Functionality of the proposed method has been tested on recorded data from Serbian electrical utility. Results demonstrate that even with a simple configuration such as this one, fair accuracy can be achieved in forecasting the hourly load. The simplicity and reusability are very important factors for installation of the proposed system in a large-scale DMS, considering the technical requirements (e.g. training data availability, processing power and memory capacity).

[1]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[2]  B. Tech,et al.  Application of Neural Network to Load Forecasting in Nigerian Electrical Power System , 2007 .

[3]  A. C. Liew,et al.  Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting , 1995 .

[4]  Farshid Keynia,et al.  Short-Term Load Forecast of Microgrids by a New Bilevel Prediction Strategy , 2010, IEEE Transactions on Smart Grid.

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

[6]  Nima Amjady,et al.  Short-term hourly load forecasting using time-series modeling with peak load estimation capability , 2001 .

[7]  Fabio Leccese,et al.  Hybrid Neural Network System for Electric Load Forecasting of Telecommunication Station , 2009 .

[8]  A Slobodan Ilic,et al.  HYBRID ARTIFICIAL NEURAL NETWORK SYSTEM FOR SHORT-TERM LOAD FORECASTING , 2012 .

[9]  P. J. Santos,et al.  Designing the input vector to ANN-based models for short-term load forecast in electricity distribution systems , 2007 .

[10]  Nemanja Stanisavljevic,et al.  Using ANN model to determine future waste characteristics in order to achieve specific waste management targets -case study of Serbia , 2011 .

[11]  G. G. Karady,et al.  Conceptual approach to the application of neural network for short-term load forecasting , 1990, IEEE International Symposium on Circuits and Systems.

[12]  Kyung-Bin Song,et al.  Hybrid load forecasting method with analysis of temperature sensitivities , 2006, IEEE Transactions on Power Systems.

[13]  Mohammad Shahidehpour,et al.  Market operations in electric power systems , 2002 .

[14]  James A. Momoh,et al.  Artificial neural network based load forecasting , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[15]  W. R. Christiaanse.  Short-Term Load Forecasting Using General Exponential Smoothing , 1971 .

[16]  S. Fan,et al.  Short-term load forecasting based on an adaptive hybrid method , 2006, IEEE Transactions on Power Systems.

[17]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[18]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[19]  S. J. Kiartzis,et al.  Short term load forecasting using fuzzy neural networks , 1995 .