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Neural Computing and Applications

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13.03.2018 | Original Article

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

verfasst von: Gaddafi Sani Shehu, Nurettin Çetinkaya

Erschienen in: Neural Computing and Applications | Ausgabe 10/2019

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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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Titel: Flower pollination–feedforward neural network for load flow forecasting in smart distribution grid
verfasst von: Gaddafi Sani Shehu
Nurettin Çetinkaya
Publikationsdatum: 13.03.2018
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 10/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3421-5

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