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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

Authors: Gaddafi Sani Shehu, Nurettin Çetinkaya

Published in: Neural Computing and Applications | Issue 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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Maria LT, Michael LA (2016) A review of the development of smart grid technologies. Renew Sustain Energy Rev 59:710–725. https://doi.org/10.1016/j.rser.2016.01.011 CrossRef

Borges CE, Penya YK, Fernández I (2013) Evaluating combined load forecasting in large power systems and smart grids. IEEE Trans Ind Inf 9(3):157–1570. https://doi.org/10.1109/TII.2012.2219063 CrossRef

De Felice M, Xin Y (2011) Short-term load forecasting with neural network ensembles: a comparative study. IEEE Comput Intell Mag 6(3):47–56. https://doi.org/10.1109/MCI.2011.941590 CrossRef

Asar A, Hassnain SR, Khattack AU (2005) A multi-agent approach to short term load forecasting problem. Int J Intell Control Syst 10(1):52–59

Swaroop R, Abdulqader HA (2012) Load forecasting for power system planning and operation using artificial neural network et al Batinah region Oman. J Eng Sci Technol 7(4):498–504

Duan P, Xie K, Guo T, Huang X (2011) Short-term load forecasting for electric power system using the PSO-SVR and FCM clustering techniques. Energies 4:173–184CrossRef

Bo-Juen C, Ming-Wei C, Chih-Jen L (2004) Load forecasting using support vector machines: a study on EUNITE competition 2001. IEEE Trans Power Syst 19(4):1821–1830CrossRef

Tanidir O, Tor OB (2015) Accuracy of ANN based day-ahead load forecasting in Turkish power system: degrading and improving factors. Neural Netw World 4(15):443–445. https://doi.org/10.14311/NNW.2015.25.02 CrossRef

Heiko H, Silja MN, Stefan P (2009) Electric load forecasting methods: tools for decision making. Eur J Oper Res 199(3):902–907CrossRef

10.

Peharda D, Hebel Z and Delimar M (2004) Forecasting data for load flow. In: Proceedings of the 12th IEEE mediterranean electrotechnical conference (IEEE Cat. No. 04CH37521), vol 3, pp 855–858. https://doi.org/10.1109/melcon.2004.1348082

11.

Ding N, Benoit C, Foggia G, Bésanger Y, Wurtz F (2016) Neural network-based model design for short-term load forecast in distribution systems. IEEE Trans Power Syst 31(1):72–81CrossRef

12.

Sinha AK, Mandal JK (1999) Hierarchical dynamic state estimator using ANN-based dynamic load prediction. IEE Proc Gener Transm Distrib 146(6):541–549CrossRef

13.

Srinivasan D, Chang CS, Liew AC (1995) Demand forecasting using fuzzy neural computation with special emphasis on weekend and public holiday forecasting. IEEE Trans Power Syst 10(4):1897–1903CrossRef

14.

Ling SH, Leung FH, Lam FHK, Lee YS, Tam PKS (2003) A novel genetic-algorithm-based neural network for short-term load forecasting. IEEE Trans Ind Electron 50(4):793–799CrossRef

15.

AlRashidi MR, El-Naggar KM (2010) Long term electric load forecasting based on particle swarm optimization. Appl Energy 87(1):320–326CrossRef

16.

Abdollah K, Haidar S, Fatemeh M (2014) A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl 41(13):6047–6056. https://doi.org/10.1016/j.eswa.2014.03.053 CrossRef

17.

Liye X, Wei S, Tulu L, Chen WA (2016) Combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting. Appl Energy 167:135–153CrossRef

18.

Jiang H, Zhang Y, Muljadi E, Zhang J, Gao W (2016) A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization. IEEE Trans Smart Grid 99:1. https://doi.org/10.1109/tsg.2016.2628061 CrossRef

19.

Pan L, Feng X, Sang F et al (2017) An improved back propagation neural network based on complexity decomposition technology and modified flower pollination optimization for short-term load forecasting. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3222-2 CrossRef

20.

Lei J, Jin T, Hao J et al (2017) Short-term load forecasting with clustering–regression model in distributed cluster. Cluster Comput. https://doi.org/10.1007/s10586-017-1198-4 CrossRef

21.

Sun X, Ouyang Z, Yue D (2017) Short-term load forecasting model based on multi-label and BPNN. In: Fei M., Ma S., Li X, Sun X, Jia L, Su Z. (eds) Advanced computational methods in life system modeling and simulation. LSMS 2017, ICSEE 2017, communications in computer and information science, vol 761. Springer, Singapore, pp 264–272. https://doi.org/10.1007/978-981-10-6370-1_26

22.

Kong W, Dong ZY, Hill DJ, Luo F, Xu Y (2017) Short-term residential load forecasting based on resident behaviour learning. IEEE Trans Power Syst 99:1. https://doi.org/10.1109/tpwrs.2017.2688178 CrossRef

23.

Cetinkaya N (2016) A new mathematical approach and heuristic methods for load forecasting in smart grid. In: 12th International conference on natural computation, fuzzy systems and knowledge discovery, Changsha, pp 1103–1107. https://doi.org/10.1109/fskd.2016.7603332

24.

Awad M, Khanna R (2015) Support vector regression. In: Efficient learning machines. Apress, Berkeley, pp 67–80. https://doi.org/10.1007/978-1-4302-5990-9_4

25.

Lee Y, Wen-Feng H, Chien-Ming H (2005) e-SSVR: a smooth support vector machine for e-Insensitive Regression. IEEE Trans Knowl Data Eng 17(5):678–685CrossRef

26.

Vrablecová P et al (2017) Smart grid load forecasting using online support vector regression. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.07.006 CrossRef

27.

YouLong Y et al (2016) An incremental electric load forecasting model based on support vector regression. Energy 113:796–808. https://doi.org/10.1016/j.energy.2016.07.092 CrossRef

28.

Sapankevych N, Sankar R (2009) Time series prediction using support vector machines: a survey. IEEE Comput Intell Mag 4:24–38. https://doi.org/10.1109/MCI.2009.932254 CrossRef

29.

Bao Y, Xiong T, Hu Z (2014) Multi-step-ahead time series prediction using multiple-output support vector regression. Neurocomputing 129:482–493. https://doi.org/10.1016/j.neucom.2013.09.010 CrossRef

30.

Ceperic E, Ceperic V, Baric A (2013) A strategy for short-term load forecasting by support vector regression machines. IEEE Trans Power Syst 28(4):4356–4364. https://doi.org/10.1109/TPWRS.2013.2269803 CrossRef

31.

Pellegrini M (2015) Short-term load demand forecasting in smart grids using support vector regression. In: IEEE 1st international forum on research and technologies for society and industry leveraging a better tomorrow (RTSI), Turin, pp 264–268. https://doi.org/10.1109/rtsi.2015.732510

32.

Yang XS, (2012) Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation 2012. Lecture notes in computer science, vol 7445, pp 240–249

33.

Yang XS (2014) Nature-inspired optimization algorithms, 1st edn. Elsevier, USAMATH

34.

Yang XS (2010) Nature-inspired metaheuristic algorithms. University of Cambridge, Cambridge

35.

Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Numer Optim 1(4):330–343MATH

36.

Eiben AE, Smit SK (2011) Parameter tuning for configuring and analysing evolutionary algorithms. Swarm Evol Comput 1(1):19–31CrossRef

37.

Yang XS, Karamanoglu M, Xingshi H (2013) Multi-objective flower algorithm for optimization. Proc Comput Sci 18:861–868CrossRef

38.

Haruna H et al (2015) A review of the applications of bio-inspired flower pollination algorithm. Proc Comput Sci 62:435–441. https://doi.org/10.1016/j.procs.2015.08.438 CrossRef

39.

Kayabekir AE, Bekdaş G, Nigdeli SM, Yang XS (2018) A Comprehensive review of the flower pollination algorithm for solving engineering problems. In: Yang XS (ed) Nature-inspired algorithms and applied optimization. Studies in computational intelligence, vol 744. Springer, Cham, pp 171–188. https://doi.org/10.1007/978-3-319-67669-2_8 CrossRef

40.

Xu S, Wang Y, Liu X (2017) Parameter estimation for chaotic systems via a hybrid flower pollination algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2890-2 CrossRef

41.

Rohit S, Urvinder S (2017) Application of mutation operators to flower pollination algorithm. Expert Syst Appl 79:112–129. https://doi.org/10.1016/j.eswa.2017.02.035 CrossRef

42.

Amer D (2015) On the performances of the flower pollination algorithm: qualitative and quantitative analyses. Appl Soft Comput 34:349–371. https://doi.org/10.1016/j.asoc.2015.05.015 CrossRef

43.

Alyasseri ZAA, Khader AT, Al-Betar MA, Awadallah MA, Yang XS (2018) Variants of the flower pollination algorithm: a review. In: Yang XS (ed) Nature-inspired algorithms and applied optimization. Studies in computational intelligence, vol 744. Springer, Cham, pp 91–118. https://doi.org/10.1007/978-3-319-67669-2_5 CrossRef

44.

Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4(2):251–257MathSciNetCrossRef

45.

Claudio C, Giuseppina A, Francesco G, Roberto M (2016) Heuristic techniques to optimize neural network architecture in manufacturing applications. Neural Comput Appl 27:2001–2015CrossRef

46.

Varun KO, Ajith A, Václav S (2017) Metaheuristic design of feedforward neural network: a review of two decades of research. Eng Appl Artif Intell 60:97–116CrossRef

47.

Zhang JR, Zhang J, Lock TM, Lyu MR (2007) A hybrid particle swarm optimization–back propagation algorithm for feedforward neural network training. Appl Math Comput 128:1026–1037MATH

48.

Seyed AM, Hashim SZM, Hossein MS (2012) Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl Math Comput 218(22):11125–11137MathSciNetMATH

49.

Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the international conference on neural networks. IEEE Press, New York, pp 11–13

Title: Flower pollination–feedforward neural network for load flow forecasting in smart distribution grid
Authors: Gaddafi Sani Shehu
Nurettin Çetinkaya
Publication date: 13-03-2018
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3421-5

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