Advertisement

Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid

Abstract

In this paper, a modified backpropagation neural network is combined with a chaos-search genetic algorithm and simulated annealing algorithm for very short term electrical energy demand prediction in deregulated power industry. Multiple modifications are carried out on the conventional backpropagation algorithm such as improvements in the momentum factor and adaptive learning rate. In the hybrid scheme, the initial parameters of the modified neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. The solution set provided by the optimized genetic algorithm is further improved by using the strong local search ability of simulated annealing algorithm. The real data of New South Wales, Australian grid, is used in the experimentation for 1-h-ahead forecast with an emphasis on data analysis and preprocessing framework. The correlation analysis is used for the identification and selection of the most influential input variables. The simulation results reveal that the proposed combination technique effectively enhanced the prediction accuracy as compared to the available conventional methods. The prediction of 1-h-ahead load demand is critically important for decision-making response of the modern smart grid system. The acceptable precision of the proposed model concludes that it can be applied in the smart grid to enhance its demand responsiveness and other intelligent features.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Abbreviations

ANN:

Artificial neural network

BP:

Backpropagation

BPNN:

Backpropagation neural network

CGA:

Chaotic genetic algorithm

CGA-MdBP:

Chaotic genetic algorithm modified backpropagation

CGASA-MdBP:

Chaotic genetic algorithm-simulated annealing modified backpropagation

CnBP:

Conventional backpropagation

DR:

Demand response

GA-MdBP:

Genetic algorithm modified backpropagation

ICT:

Information and communications technology

IVS:

Input variable selection

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

MSE:

Mean square error

MdBP:

Modified backpropagation

MLP:

Multilayer perceptron

MLPNN:

Multilayer perceptron neural network

NN:

Neural network

SA:

Simulated annealing

SG:

Smart grid

References

  1. 1.

    Reddy KS et al (2014) A review of integration, control, communication and metering (ICCM) of renewable energy based smart grid. Renew Sustain Energy Rev 38:180–192

  2. 2.

    Hernández L et al (2013) Improved short-term load forecasting based on two-stage predictions with artificial neural networks in a microgrid environment. Energies 6(9):4489–4507

  3. 3.

    Alfares HK, Nazeeruddin M (2002) Electric load forecasting: literature survey and classification of methods. Int J Syst Sci 33(1):23–34

  4. 4.

    Kandil N et al (2006) An efficient approach for short term load forecasting using artificial neural networks. Int J Electr Power Energy Syst 28(8):525–530

  5. 5.

    Bhatt J, Shah V, Jani O (2014) An instrumentation engineer’s review on smart grid: critical applications and parameters. Renew Sustain Energy Rev 40:1217–1239

  6. 6.

    Górriz JM et al (2004) A new model for time-series forecasting using radial basis functions and exogenous data. Neural Comput Appl 13(2):101–111

  7. 7.

    Marín F, Sandoval F (1997) Short-term peak load forecasting: statistical methods versus artificial neural networks. In: Biological and artificial computation: from neuroscience to technology. Springer, Berlin, Heidelberg, pp 1334–1343

  8. 8.

    Yun K et al (2012) Building hourly thermal load prediction using an indexed ARX model. Energy Build 54:225–233

  9. 9.

    Asber D et al (2007) Non-parametric short-term load forecasting. Int J Electr Power Energy Syst 29(8):630–635

  10. 10.

    Lowry G, Bianeyin FU, Shah N (2007) Seasonal autoregressive modelling of water and fuel consumptions in buildings. Appl Energy 84(5):542–552

  11. 11.

    Wu J et al (2013) Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model. Energy Convers Manag 70:1–9

  12. 12.

    Chakhchoukh Y, Panciatici P, Mili L (2011) Electric load forecasting based on statistical robust methods. IEEE Trans Power Syst 26(3):982–991

  13. 13.

    Rahman S, Hazim O (1996) Load forecasting for multiple sites: development of an expert system-based technique. Electr Power Syst Res 39(3):161–169

  14. 14.

    Sarrias-Mena R et al (2014) Fuzzy logic based power management strategy of a multi-MW doubly-fed induction generator wind turbine with battery and ultracapacitor. Energy 70:561–576

  15. 15.

    Chan ZSH et al (2006) Short-term ANN load forecasting from limited data using generalization learning strategies. Neurocomputing 70(1–3):409–419

  16. 16.

    Ahmadizar F et al (2015) Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm. Eng Appl Artif Intell 39:1–13

  17. 17.

    Suganthi L, Samuel AA (2012) Energy models for demand forecasting—a review. Renew Sustain Energy Rev 16(2):1223–1240

  18. 18.

    Catoni O (1996) Metropolis, simulated annealing, and iterated energy transformation algorithms: theory and experiments. J Complex 12(4):595–623

  19. 19.

    Liu Z, Sun W, Zeng J (2013) A new short-term load forecasting method of power system based on EEMD and SS-PSO. Neural Comput Appl 24(3):973–983

  20. 20.

    Abd-Elazim SM, Ali ES (2013) Power system stability enhancement via bacteria foraging optimization algorithm. Arab J Sci Eng 38(3):599–611

  21. 21.

    Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, Cambridge, MA

  22. 22.

    Oshaba A, Ali E (2014) Bacteria foraging: a new technique for speed control of DC series motor supplied by photovoltaic system. Int J WSEAS Trans Power Syst 9:185–195

  23. 23.

    Ali E, Abd-Elazim S (2013) Synergy of particle swarm optimization and bacterial foraging for SSSC damping controller design. Int J WSEAS Trans Power Syst 8(2):74–84

  24. 24.

    Yang Y, Zheng G, Liu D (2001) BP-GA mixed algorithms for short-term load forecasting. 0-7803-70 10-4/01/IEEE

  25. 25.

    Zhangang Y, Yanbo C, Cheng KE (2007) Genetic algorithm-based RBF neural network load forecasting model. In: Power engineering society general meeting, 2007. IEEE

  26. 26.

    Yu F, Xu X (2014) A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Appl Energy 134:102–113

  27. 27.

    Ling S et al (2002) Short-term daily load forecasting in an intelligent home with GA-based neural network. In: Proceedings of the 2002 international joint conference on neural networks, 2002 IJCNN’02. IEEE

  28. 28.

    Liao G-C, Tsao T-P (2006) Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting. IEEE Trans Evol Comput 10(3):330–340

  29. 29.

    Yang K-H, Shan G-L, Zhao L-L (2006) Correlation coefficient method for support vector machine input samples. In: International conference on machine learning and cybernetics, 2006. IEEE

  30. 30.

    Xiao L et al (2015) A hybrid model based on data preprocessing for electrical power forecasting. Int J Electr Power Energy Syst 64:311–327

  31. 31.

    de Aquino RR et al (2006) Combining artificial neural networks and heuristic rules in a hybrid intelligent load forecast system. In: Artificial neural networks—ICANN 2006. Springer, pp 757–766

  32. 32.

    Jing G, Du W, Guo Y (2012) Studies on prediction of separation percent in electrodialysis process via BP neural networks and improved BP algorithms. Desalination 291:78–93

  33. 33.

    Simon H (2009) Neural networks and learning machines. Pearson International Edition, Pearson, Bostan, MA, p 282

  34. 34.

    Nayak S, Misra B, Behera H (2012) Index prediction with neuro-genetic hybrid network: a comparative analysis of performance. In 2012 International conference on computing, communication and applications (ICCCA). IEEE

  35. 35.

    Zhangang Y, Yanbo C, Cheng KE (2007) Genetic algorithm-based RBF neural network load forecasting model. In: Power Engineering Society General Meeting. IEEE, pp 1–6

  36. 36.

    Shapiro AF (2002) The merging of neural networks, fuzzy logic, and genetic algorithms. Insur Math Econ 31(1):115–131

  37. 37.

    Ghayekhloo M, Menhaj M, Ghofrani M (2015) A hybrid short-term load forecasting with a new data preprocessing framework. Electr Power Syst Res 119:138–148

  38. 38.

    Wang J-J et al (2012) Stock index forecasting based on a hybrid model. Omega 40(6):758–766

Download references

Acknowledgments

The authors wish to thank Universiti Teknologi PETRONAS for providing the research Grant (Number URIF 0153AA-B13) to conduct this research.

Author information

Correspondence to Badar Islam.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Islam, B., Baharudin, Z. & Nallagownden, P. Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid. Neural Comput & Applic 28, 877–891 (2017). https://doi.org/10.1007/s00521-016-2408-3

Download citation

Keywords

  • Artificial neural network
  • Demand response
  • Smart grid
  • Real-coded genetic algorithm
  • Electrical energy demand prediction
  • Chaotic mapping
  • Simulated annealing