Abstract
This paper illustrates an integrated Computational Intelligence (CI) technique using Artificial Neural Networks (ANN) and Genetic Algorithms (GA) for Electric Load Forecasting. A load forecasting model has been developed based on ANN and GA. The model produces a short-term forecast of the load in the 24 hours of the forecast day concerned. Optimum weights and the biases of ANN are found by the Genetic Algorithm. The technique has been tested on data provided by an Italian power company and the results obtained through the application of integrated computational intelligence approach show that this approach is not practical without high computational facilities as this problem is very complex. However, this points to the direction of evolutionary computing being integrated with parallel processing techniques to solve practical problems...
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References
Gross, G., Galiana, F.D.: Short term load forecasting. Proc. of IEEE, Vol. 75,No. 12, 1987, pp. 1558–1573
Hagan, M.T., S M Behr, S.M.: The time series approach to short term load forecasting. IEEE Transactions on Power Systems, Vol. 2,No. 3, 1987, pp. 785–791
Papalexopoulos, A.D., Hesterberg, T.C.: A regression-based approach to short-term load forecasting. IEEE Transactions on Power Systems, Vol. 5,No. 4, 1990, pp. 1535–1547
Rahman, S., Bhantnagar, R.: An expert system based algorithm for short-term load forecast. IEEE Transactions on Power Systems, Vol. 3,No. 2, 1988, pp. 392–399
Dhdashti, A.S., Tudor, J.R., Smith, M.C.: Forecasting of hourly load by pattern recognition: a deterministic approach. Transactions on Power Apparatus and Systems, Vol. 101, 1982, pp. 900–910
Caciotta, M., Lamedica, R., Cencelli, V.O., Prudenzi, A., Sforna, M.: Application of artificial neural networks to historical data analysis for short-term electric load forecasting. European Transactions on Electrical Power, Vol. 7, 1997, pp. 49–56
Maifield, T., Sheble, G.: Short term load forecasting by neural network and a refined genetic algorithm. Electrical Power Systems Research, Vol. 31, pp. 9–14.
Lai, L.L., Sichanie, A.G., Rajkumar, N., Styvaktakis, E., Sforna, M., Caciotta, M.: Practical application of object oriented techniques to designing neural networks for short-term electric load forecasting. Proceedings of the Energy Management and Power Delivery Conference, IEEE Catalogue No 98EX137, March 1998, pp. 559–563
Heng, E.T.H., Srinivasan, D., Liew, A.C.: Short term load forecasting using genetic algorithm and neural networks. Proceedings of the Energy Management and Power Delivery Conference, IEEE Catalogue No 98EX137, March 1998, pp. 576–581
Lai, L.L.: Intelligent system applications in power engineering — evolutionary programming and neural networks. John Wiley and Sons, 1998
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© 1999 Springer-Verlag Berlin Heidelberg
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Lai, L.L., Subasinghe, H., Rajkumar, N., Vaseekar, E., Gwyn, B.J., Sood, V.K. (1999). Object-Oriented Genetic Algorithm Based Artificial Neural Network for Load Forecasting. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_59
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DOI: https://doi.org/10.1007/3-540-48873-1_59
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