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Long-term electric energy consumption forecasting via artificial cooperative search algorithm

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  • Kaboli, S. Hr. Aghay
  • Selvaraj, J.
  • Rahim, N.A.

Abstract

This study mathematically formulates the effects of socio-economic indicators (gross domestic production, population, stock index, export, and import) on Iran's electric energy consumption. The path-coefficient analysis is implemented on linear, quadratic, exponential, and logarithmic models to determine the optimized weighting factors. On this basis, artificial cooperative search algorithm is developed to provide better-fit solution and improve the accuracy of estimation. Artificial cooperative search algorithm is a recently developed evolutionary algorithm with high probability of finding optimal solution in complex optimization problems. This merit is provided by balancing exploitation of better results and exploration of the problem's search space through use of a single control parameter and two advanced crossover and mutation operators. To assess the applicability and accuracy of the proposed method, it is compared with genetic algorithm, particle swarm optimization, imperialist competitive algorithm, cuckoo search, simulated annealing, and differential evolution. The simulation results are validated by actual data sets obtained from 1992 until 2013. The results confirm the higher accuracy and reliability of the proposed method in electric power consumption forecasting as compared with other optimization methods. Future estimation of Iran's electric energy consumption is then projected up to 2030 according to three different scenarios.

Suggested Citation

  • Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
    • Handle: RePEc:eee:energy:v:115:y:2016:i:p1:p:857-871
    DOI: 10.1016/j.energy.2016.09.015
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    References listed on IDEAS

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