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

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Abstract

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.

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Acknowledgements

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)

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Authors and Affiliations

  1. Graduate School of Natural and Applied Science, Selçuk University, Alaeddin Keykubat Yerleşkesi, Akademi Mah. Yeni İstanbul Cad. No: 369, 42130, Selçuklu, Konya, Turkey

    Gaddafi Sani Shehu

  2. Electrical and Electronics Engineering Department, Selçuk University, Alaeddin Keykubat Yerleşkesi, Akademi Mah. Yeni İstanbul Cad. No: 369, 42130, Selçuklu, Konya, Turkey

    Nurettin Çetinkaya

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  1. Gaddafi Sani Shehu
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Correspondence to Gaddafi Sani Shehu.

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Shehu, G.S., Çetinkaya, N. Flower pollination–feedforward neural network for load flow forecasting in smart distribution grid. Neural Comput & Applic 31, 6001–6012 (2019). https://doi.org/10.1007/s00521-018-3421-5

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  • DOI: https://doi.org/10.1007/s00521-018-3421-5

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