Short Term Load Forecasting Based on Hybrid ANN and PSO

Short term load forecasting (STLF) is the prediction of electrical load for a period that ranges from one hour to a week. The main objectives of the (STLF) are to predict future load for the generation scheduling at power stations; assess the security of the power system as well as for timely dispatching of electrical power. The traditional load forecasting tools utilize time series models which extrapolate historical load data to predict the future loads. These tools assume a static load series and retain normal distribution characteristics. Due to their inability to adapt to changing environments and load characteristics, they often lead to large forecasting errors. In an effort to reduce the forecasting error, hybrid artificial neural network (ANN) and particle swarm optimization (PSO) is used in this paper.It is shown that the hybridization of ANN and PSO gives better resultscompared to the standard ANN with back propagation.

[1]  G. Gross,et al.  Short-term load forecasting , 1987, Proceedings of the IEEE.

[2]  F. Grimaccia,et al.  PSO as an effective learning algorithm for neural network applications , 2004, Proceedings. ICCEA 2004. 2004 3rd International Conference on Computational Electromagnetics and Its Applications, 2004..

[3]  Riccardo Poli,et al.  Particle Swarm Optimisation , 2011 .

[4]  N. Kandil,et al.  Use of ANNs for short-term load forecasting , 1999, Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411).

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

[6]  P. Subbaraj,et al.  Evolutionary Techniques Based Combined Artificial Neural Networks for Peak Load Forecasting , 2008 .

[7]  W. Charytoniuk,et al.  Very short-term load forecasting using artificial neural networks , 2000 .

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

[9]  Michael T. Manry,et al.  Comparison of very short-term load forecasting techniques , 1996 .

[10]  Abdulaziz . Alshareef,et al.  Next 24-Hours Load Forecasting for the Western Area of Saudi Arabia Using Artificial Neural Network and Particle Swarm Optimization , 2010 .

[11]  Jing Liu,et al.  Grouping model application on artificial neural networks for short-term load forecasting , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[12]  Jorge J. Gómez-Sanz,et al.  A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework , 2012, Sensors.

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

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

[15]  Moon-Hee Park,et al.  Short-term Load Forecasting Using Artificial Neural Network , 1992 .

[16]  Rahman Saidur,et al.  Artificial Neural Network based Short Term Load Forecasting of Power System , 2011 .

[17]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[18]  M. Talaat,et al.  A New Approach for Short-Term Load Forecasting Using Curve Fitting Prediction Optimized by Genetic Algorithms , 2012 .

[19]  Hossein Shayeghi,et al.  STLF Based on Optimized Neural Network Using PSO , 2009 .

[20]  Amit Jain,et al.  A novel hybrid method for short term load forecasting using fuzzy logic and particle swarm optimization , 2010, 2010 International Conference on Power System Technology.

[21]  J. K. Mandal,et al.  Application of recurrent neural network for short term load forecasting in electric power system , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[22]  Kwang-Ho Kim,et al.  Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems , 1995 .

[23]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[24]  Komla A. Folly,et al.  Performance evaluation of a PBIL-based power system damping controller , 2010, IEEE Congress on Evolutionary Computation.

[25]  J. R. McDonald,et al.  Experience with artificial neural network models for short-term load forecasting in electrical power systems: a proposed application of expert networks , 1993 .

[26]  Siddarameshwara N.,et al.  Electricity Short Term Load Forecasting Using Elman Recurrent Neural Network , 2010, 2010 International Conference on Advances in Recent Technologies in Communication and Computing.

[27]  A.A. El-Keib,et al.  A review of ANN-based short-term load forecasting models , 1995, Proceedings of the Twenty-Seventh Southeastern Symposium on System Theory.

[28]  A. C. Liew,et al.  Fuzzy neural network and fuzzy expert system for load forecasting , 1996 .