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

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Küçük Resim

Tarih

2019

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

SPRINGER LONDON LTD

Erişim Hakkı

info:eu-repo/semantics/openAccess

Özet

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.

Açıklama

Anahtar Kelimeler

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

Kaynak

NEURAL COMPUTING & APPLICATIONS

WoS Q Değeri

Q1

Scopus Q Değeri

Q1

Cilt

31

Sayı

10

Künye

Shehu, G. S., Çetinkaya, N. (2019). Flower Pollination–Feedforward Neural Network for Load Flow Forecasting in Smart Distribution Grid. Neural Computing and Applications, 31(10), 6001-6012.