Evolutionary neural-networks based optimisation for short-term load forecasting

• Autorzy: Grzenda M. , Macukow B.
• Języki publikacji: EN

Abstrakty

EN

The purpose of short-term load forecasting is to optimise the power supply volume in short time horizon. There is no straightforward mapping rule between the type of time period and the resulting power consumption. Still, it is inevitable for the overall efficiency of the power system to rely on a good prediction model. Our paper illustrates a novel approach based on evolutionary programming. Feedforward networks are being evolved by the ECoMLP method in order to properly solve the optimisation problem, defined as minimisation of the prediction error. All the results have been obtained using the data from the Polish Power System. The data used for the training and tests has been chosen so as to reflect both short-time and long-time dependencies between time period category and load of the system. The primary feature of the described method is a novel self-adaptive procedure that is a part of a sophisticated design algorithm serving to select both network architecture and weight connections. Due to the application of this procedure, no time consuming tests are required to train and retrain neural prediction models. Therefore, the method makes it possible to construct and maintain prediction models for load forecasting without expert knowledge about neural networks.

Słowa kluczowe

EN

evolutionary programming; neural networks; optimisation

PL

optymalizacja; programowanie ewolucyjne; sieć neuronowa

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2002
• Tom: Vol. 31, no. 2
• Strony: 371--382
• Opis fizyczny: Bibliogr. 11 poz.,

Twórcy

autor

Grzenda M.

• Warsaw University of Technology, Faculty of Mathematics and Information Science, Plac Politechniki 1, 00-661 Warszawa, Poland

autor

Macukow B.

• Warsaw University of Technology, Faculty of Mathematics and Information Science, Plac Politechniki 1, 00-661 Warszawa, Poland

Bibliografia

• BACK, T. and SCHWEFEL, H.P. (1993) An Overview of Evolutionary Algorithms for Parameter Optimization, Evolutionary Computation, 1, 1, 1-23.
• BACK, T. (1998) An Overview of Parameter Control Methods by Self-Adaptation in Evolutionary Algorithms. Hmdarnenta Informaticae, 35, 51 - 66.
• DOVEH, E. et al. (1999) Experience with FNN Models for Medium Term Power Demand Predictions. IEEE Transactions on Power Systems, 538- 546.
• FOGEL, D.B. (1999) An Overview of Evolutionary Programming. The IMA Volumes in Mathematics and its Applications, 111, 89 - 109.
• GRZENDA, M. (2001) Application of evolutionary programming in the design of multilayer networks. Ph.D. thesis (in Polish), Warsaw University of Technology.
• GRZENDA, M. and MACUKOW, B. (2000) The Role of Weight Domain in Evolutionary Design of Multilayer Perceptrons. Proc. of the IEEE-INNS-ENNS Intemational Joint Conference on Neural Networks IJCNN 2000.
• HAYKIN, S. (1999) Neural Networks: a Comprehensive Foundation. Prentice-Hall Inc.
• Lor, L.L. (1998) Intelligent System Applications in Power Engineering. Evolutionary Programming and Neuml Networks. John Wiley and Sons.
• MACUKOW, B. and GRZENDA, M. (2001) Towards the Evolution of Neural Networks. Opto-Electronics Review, 9 (3), 316- 319.
• OSOWSKI, S. and SIWEK, K. (1998) Selforganizing Neural Networks for Short Term Load Forecasting in Power System. Engineering Applications of Neural Networks (EANN), 235-268.
• SRINIVASAN, D., CHANG C.S. and LIEW, A.C. (1995) Demand Forecasting Using Fuzzy Neural Computation, With Special Emphasis on Weekend and Public Holiday Forecasting. IEEE Transactions on Power Systems, 10, 4, 1897- 1903.