Title
Flower pollination–feedforward neural network for load flow forecasting in smart distribution grid
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.
Year
DOI
Venue
2019
10.1007/s00521-018-3421-5
Neural Computing and Applications
Keywords
Field
DocType
Flower pollination algorithm, Feedforward neural network, Load flow forecasting, Smart distribution grid
Population,Mathematical optimization,Feedforward neural network,Premature convergence,Support vector machine,Intermittency,Distribution management system,Artificial neural network,Mathematics,Metaheuristic
Journal
Volume
Issue
ISSN
31.0
10
1433-3058
Citations 
PageRank 
References 
1
0.36
21
Authors
2
Name
Order
Citations
PageRank
Shehu, Gaddafi S140.82
Nurettin Çetinkaya291.87