Flower pollination–feedforward neural network for load flow forecasting in smart distribution grid

Original Article


Nature-inspired population-based metaheuristic flower pollination algorithm is proposed in solving load flow forecasting problem in smart distribution grid environment. The efficient approach involves training a feedforward neural network (FNN) with a new flower pollination algorithm (FPA). The idea is to perform short-term load flow forecasting in smart distribution network, thus maintaining system security due to intermittency of renewable energy penetration and power flow demand. Application of optimization algorithms such as FPA in training neural network improves accuracy, overcomes generalization ability of neural network, requires less data and prevents premature convergence problem in artificial intelligence solutions due to nonlinearity of parameters. The real load flow data are collected through distribution management system of Konya Organized Industrial Zone. The result obtained indicates strong improvement in error reduction using flower pollination optimization algorithm in training FNN for short-term load flow forecasting in smart distribution grid; the model is compared against FNN model and efficient support vector regression.


Flower pollination algorithm Feedforward neural network Load flow forecasting Smart distribution grid 



The authors acknowledge the effort of Konya Organized Industrial Zone Directorate for providing access to system data, and support of Scientific and Technological Research Council of Turkey (TUBITAK)

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Graduate School of Natural and Applied ScienceSelçuk UniversitySelçukluTurkey
  2. 2.Electrical and Electronics Engineering DepartmentSelçuk UniversitySelçukluTurkey

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