Short Term Load Forecasting (STLF) Using Generalized Neural Network (GNN) Trained with Adaptive GA

  • D. K. Chaturvedi
  • Sinha Anand Premdayal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)


The paper is mainly focus to develop an integration of GNN and wavelet based models for STLF. The model is trained by using error back-propagation algorithm, but there are certain inherent drawbacks of back-propagation algorithm. To overcome the drawbacks of back propagation algorithm such as slow learning, stuck in local minima, needs error gradient etc. genetic algorithm (GA) is proposed. The performance of GA is further improved by making an adaptive GA with the help of fuzzy system. The adaptive GA changes the GA parameters such as cross over probability and mutation rate during execution by using fuzzy system. The GNN-W-AGA is used to forecast electrical load and compared with GNN-W trained with backprop and actual data.


Load Forecasting GNN Wavelet Adaptive GA Fuzzy System 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amjady, N.: Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Transactions on Power Systems 16(3), 498–505 (2001)CrossRefGoogle Scholar
  2. 2.
    Papalexopoulos, A.D., Hesterberg, T.C.: A Regression Based Approach to Short Term Load Forecasting. IEEE Transactions on Power Systems 5(1), 40–45 (1990)CrossRefGoogle Scholar
  3. 3.
    Christiaanse, W.R.: Short term Load Forecasting using General Exponential Smoothing. IEEE Trans. on PAS PAS – 90(2), 900–910 (1971)CrossRefGoogle Scholar
  4. 4.
    Villalba, S.A., Bel, C.A.: Hybrid demand model for load estimation and short-term load forecasting in distribution electrical systems. IEEE Transactions on Power Delivery 15(2), 764–769 (2000)CrossRefGoogle Scholar
  5. 5.
    Yang, J., Cheng, H.: Application of SVM to power system short-term load forecast. Power System Automation Equipment China 24(4), 30–32 (2004)Google Scholar
  6. 6.
    Hwan, K.J., Kim, G.W.: A short-term load forecasting expert system. In: Proceedings of the Fifth Russian-Korean International Symposium on Science and Technology, pp. 112–116 (2001)Google Scholar
  7. 7.
    Desouky, A.A., Elkateb, M.M.: Hybrid adaptive techniques for electric-load forecast using ANN and ARIMA. IEE Proceedings of Generation, Transmission and Distribution 147(4), 213–217 (2000)CrossRefGoogle Scholar
  8. 8.
    Kim, K.H., Youn, H.S., Kang, Y.C.: Short-term load forecasting for special days in anomalous load conditions using neural networks and fuzzy inference method. IEEE Transactions on Power Systems 15(2), 559–565 (2000)CrossRefGoogle Scholar
  9. 9.
    Bunn, D.W.: Forecasting loads and prices in competitive power markets. Proceedings of the IEEE 88, 163–169 (2000)CrossRefGoogle Scholar
  10. 10.
    Chaturvedi, D.K., Satsangi, P.S., Kalra, P.K.: Fuzzified Neural Network Approach for Load Forecasting Problems. Int. J. on Engineering Intelligent Systems 9(1), 3–9 (2001)Google Scholar
  11. 11.
    Chaturvedi, D.K., Kumar, R., Mohan, M., Kalra, P.K.: Artificial Neural Network learning using improved Genetic algorithm. J. IE(I), EL 82 (2001)Google Scholar
  12. 12.
    Chaturvedi, D.K., Satsangi, P.S., Kalra, P.K.: Load Frequency Control: A Generalized Neural Network Approach. Electric Power and Energy Systems 21, 405–415 (1999)CrossRefGoogle Scholar
  13. 13.
    Mizumoto, M.: Pictorial representations of fuzzy connectives, Part II: cases of compensatory operators and self-dual operators. Fuzzy Sets and Systems 32, 45–79 (1989)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Chaturvedi, D.K., Mohan, M., Singh, R.K., Kalra, P.K.: Improved Generalized Neuron Model for Short Term Load Forecasting. Int. J. on Soft Computing - A Fusion of Foundations, Methodologies and Applications 8(1), 10–18 (2004)Google Scholar
  15. 15.
    Chaturvedi, D.K.: Soft Computing Techniques and its applications in Electrical Engineering. SCI, vol. 103. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  16. 16.
    Huang, C.-M., Yang, H.T.: Evolving wavelet-based networks for short term load forecasting. Proc. Inst. Elect. Eng., Gen., Transm., Distrib. 148(3), 222–228 (2001)CrossRefGoogle Scholar
  17. 17.
    Oonsivilai, A., El-Hawary, M.E.: Wavelet neural network based short-term load forecasting of electric power system commercial load. In: Proc. IEEE Can. Conf. Electrical and Computer Engineering, pp. 1223–1228 (1999)Google Scholar
  18. 18.
    Chang, C.S., Fu, W., Yi, M.: Short term load forecasting using wavelet networks. Eng. Intell. Syst. Elect. Eng. Commun. 6, 217–223 (1998)Google Scholar
  19. 19.
    Chenthur Pandian, S., Duraiswamy, K., Christober Asir Rajan, C., Kanagaraj, N.: Fuzzy approach for short term load forecasting. Electric Power Systems Research 76, 541–548 (2006)CrossRefGoogle Scholar
  20. 20.
    Banakar, A., Azeem, A.: Artificial Wavelet Neural Network and its application in Neurofuzzy models. Elsevier Applied Soft Computing (2008)Google Scholar
  21. 21.
    Ho, D.W.C., Zhang, P.A., Xu, J.: Fuzzy wavelet networks for function learning. IEEE Transactions on Fuzzy Systems 9, 200–211 (2001)CrossRefGoogle Scholar
  22. 22.
    Chaturvedi, D.K., Das, V.S.: Optimization of Genetic Algorithm Parameters. In: National Conference on Applied Systems Engineering and Soft Computing (SASESC 2000), pp. 194–198. Organized by Dayalbagh Educational Institute, Dayalbagh (2000)Google Scholar
  23. 23.
    Fogarty, T.C.: Varying the Probability of Mutation in the Genetic Algorithm. In: Proceedings of 3rd International Conference in Genetic Algorithms and Applications, Arlington, VA, pp. 104–109 (1981)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • D. K. Chaturvedi
    • 1
  • Sinha Anand Premdayal
    • 1
  1. 1.Dept. of Electrical Engineering, Faculty of EngineeringD.E.I. DayalbaghAgraIndia

Personalised recommendations