##
**Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption.**
*(English)*
Zbl 1222.78031

Summary: This study presents an integrated genetic algorithm (GA) and artificial neural network (ANN) to estimate and predict electricity demand using stochastic procedures. The economic indicators used in this paper are price, value added, number of customers and consumption in the previous periods. This model can be used to estimate energy demand in the future by optimizing parameter values. The GA applied in this study is tuned for all its parameters and the best coefficients with minimum error are identified, while all parameter values are tested concurrently. The estimation errors of genetic algorithm model are less than that of estimated by regression method. ANN is used to forecast each independent variable and then electricity consumption is forecasted up to year 2008. It is shown that the integrated GA and ANN dominate time series approach from the point of yielding less MAPE (Mean Absolute Percentage Error) error. In addition, another unique feature of this study is the utilization of ANN instead of time series to obtain better predictions for energy consumption. Electricity consumption in the Iranian agriculture sector from 1981 to 2005 is considered as the case of this study.

### MSC:

78A55 | Technical applications of optics and electromagnetic theory |

78M25 | Numerical methods in optics (MSC2010) |

PDFBibTeX
XMLCite

\textit{A. Azadeh} et al., Appl. Math. Comput. 186, No. 2, 1731--1741 (2007; Zbl 1222.78031)

Full Text:
DOI

### References:

[1] | Ceylan, H.; Ozturk, H., Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach, Energy Conversion and Management, 45, 2525-2537 (2004) |

[2] | Ozturk, H.; Ceylan, H.; Canyurt, O. E.; Hepbasli, A., Electricity estimation using genetic algorithm approach: a case study of Turkey, Energy, 30, 1003-1012 (2003) |

[3] | Osman, M. S.; Abo Sinna, M. A.; Mousa, A. A., A combined genetic algorithm-fuzzy logic controller (GA-FLC) in non-linear programming, Applied Mathematics and Computations, 821-840 (2005) · Zbl 1103.93349 |

[4] | Bunning, D.; Sun, M., Genetic algorithm for constrained global optimization in continuous variables, Applied Mathematics and Computation, 171, 604-636 (2005) · Zbl 1090.65067 |

[5] | Tang, A.; Quek, C.; Ng, G., GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms, Expert Systems with Applications, 29, 769-781 (2005) |

[6] | Stach, W.; Kurgan, L.; Pedricz, W.; Reformat, M., Genetic learning off fuzzy cognitive maps, Fuzzy Sets and Systems, 153, 371-401 (2005) · Zbl 1074.68592 |

[7] | Muni, D.; Pal, N.; Das, J., Genetic programming for simultaneous feature selection and classifier design, IEEE Transactions on Systems, Man and Cybernetics, 36, 1 (2006) |

[8] | Canyurt, O. E.; Ozturk, H.; Hepbasli, A., Energy demand estimation based on two-different genetic algorithm approaches, Energy Source, 26, 14, 1313-1320 (2004) |

[9] | Haldenbilen, S.; Ceylan, H., Genetic algorithm approach to estimate transport energy demand in Turkey, Energy Policy, 89-98 (2004) |

[10] | Goldberg, D. E., Genetic Algorithm in Search, Optimization and Machine Learning (1989), Addison-Wesley: Addison-Wesley Harlow · Zbl 0721.68056 |

[11] | VanderNoot, T. J.; Abrahams, I., The use of genetic algorithms in the non-linear regression of imittance data, Journal of Electro analytical Chemistry, 448, 17-23 (1998) |

[12] | Ozturk, H.; Canyurt, H.; Hepbasli, A.; Utlu, Z., Three different genetic algorithm approaches to the estimation of residential energy input/output values, Building and Environment, 39, 807-816 (2003) |

[17] | Box, G. E.P.; Jenkins, G. M.; Reinsel, G. C., Time Series Analysis: Forecasting and Control (1994), Prentice-Hall: Prentice-Hall Englewood Cliffs, NJ · Zbl 0858.62072 |

[18] | Abdallah, J.; Al-Thamier, A., The economic-environmental neural network model for electrical power dispatching, Journal of Applied Sciences, 4, 340-343 (2004) |

[20] | Rumelhart, D. E.; McClelland, J. L., Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Foundations, vol. 1 (1986), MIT Press: MIT Press Cambridge, MA |

[21] | Hipperta, H. S.; Bunnb, D. W.; Souza, R. C., Large Neural Networks for electricity load forecasting: are they over fitted?, International Journal of Forecasting, 12, 10-20 (2004) |

[22] | Darbellay, G. A.; Slama, M., Forecasting the short-term demand for electricity: do neural networks stand a better chance?, International Journal of Forecasting, 16, 71-83 (2000) |

[24] | Nogales, F. J.; Contreras, J.; Conejo, A. J., Forecasting next-day electricity prices by time series models, IEEE Transactions on Power Systems, 17, 2 (2002) |

[25] | Nielsen, F., Neural Networks-Algorithms and Application (2001), Niels Brock Business College |

[26] | Bao, J., Short-Term Load Forecasting based on Neural Network and Moving Average (2001), Artificial Intelligence Laboratory, Department of Computer Science |

[27] | Mohammed, O.; Park, D.; Merchant, R.; Dinh, T.; Tong, C.; Azeem, A.; Farah, J.; Drake, C., Practical experiences with an adaptive neural network short-term load forecasting system, IEEE Transactions on Power Systems, 10, 1, 254-265 (1995) |

This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.