Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

Chen, Xuan; Przystupa, Krzysztof; Ye, Zhiwei; Chen, Feng; Wang, Chunzhi; Liu, Jinhang; Gao, Rong; Wei, Ming; Kochan, Orest

doi:10.17531/ein.2022.1.17

Artykuł - szczegóły

Tytuł artykułu

Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

Autorzy

Chen Xuan , Przystupa Krzysztof , Ye Zhiwei , Chen Feng , Wang Chunzhi , Liu Jinhang , Gao Rong , Wei Ming , Kochan Orest

Treść / Zawartość

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DOI

10.17531/ein.2022.1.17

Warianty tytułu

Języki publikacji

Abstrakty

In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.

Słowa kluczowe

short-term electric load forecast extreme learning machine Lévy flight tree-seed algorithm kernel principal component analysis

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2022

Tom

Vol. 24, no. 1

Strony

153--162

Opis fizyczny

Bibliogr. 65 poz., rys., tab.

Twórcy

autor

Chen Xuan

xuan927@hbut.edu.cn

Hubei University of Technology, School of Computer Science, Wuhan, Hubei, 430068, PR China

https://orcid.org/0000-0003-1343-7132

autor

Przystupa Krzysztof

k.przystupa@pollub.pl

Lublin University of Technology, Department of Automation, ul. Nadbystrzycka 36, 20-618 Lublin, Poland

https://orcid.org/0000-0003-4361-2763

autor

Ye Zhiwei

hgcsyzw@mail.hbut.edu.cn

Hubei University of Technology, School of Computer Science, Wuhan, Hubei, 430068, PR China

https://orcid.org/0000-0001-6668-4634

autor

Chen Feng

chenfeng4179@fiberhome.com

Hubei University of Technology, School of Computer Science, Wuhan, Hubei, 430068, PR China

https://orcid.org/0000-0001-9517-1301

autor

Wang Chunzhi

chunzhiwang@mail.hbut.edu.cn

Hubei University of Technology, School of Computer Science, Wuhan, Hubei, 430068, PR China

https://orcid.org/0000-0002-6742-3644

autor

Liu Jinhang

liujinhang@hbut.edu.cn

Hubei University of Technology, School of Computer Science, Wuhan, Hubei, 430068, PR China

https://orcid.org/0000-0002-5509-0846

autor

Gao Rong

gaorong@hbut.edu.cn

Hubei University of Technology, School of Computer Science, Wuhan, Hubei, 430068, PR China

https://orcid.org/0000-0001-7935-7173

autor

Wei Ming

mwei@fiberhome.com

Wuhan Fiberhome Technical Services Co., Ltd., Wuhan, Hubei, 430074, PR China
Wuhan FiberHome Telecommunication Technologies Co., Ltd., Wuhan, Hubei, 430074, PR China

https://orcid.org/0000-0003-4695-5657

autor

Kochan Orest

orest.v.kochan@lpnu.ua

Hubei University of Technology, School of Computer Science, Wuhan, Hubei, 430068, PR China
Lviv Polytechnic National University, Bandery 12, 79013 Lviv, Ukraine

https://orcid.org/0000-0002-3164-3821

Bibliografia

1. Čepin M. Evaluation of the importance factors of the power plants within the power system reliability evaluation. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(4): 631-637, https://doi.org/10.17531/ein.2019.4.12.
2. Barshandeh S, Haghzadeh M. A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Engineering with Computers 2020: 1-44, https://doi.org/10.1007/s00366-020-00994-0.
3. Chen F. Research on Short-term Power Load Forecasting Based on Hybrid Intelligent Optimization Algorithm. Master thesis, Hubei University of Technology, 2020.
4. Chen Y, Kloft M, Yang Y et al. Mixed kernel based extreme learning machine for electric load forecasting. Neurocomputing 2018; 312: 90-106, https://doi.org/10.1016/j.neucom.2018.05.068.
5. Dash R, Dash PK, Bisoi R. A self adaptive differential harmony search based optimized extreme learning machine for financial time series prediction. Swarm and Evolutionary Computation 2014; 19: 25-42, https://doi.org/10.1016/j.swevo.2014.07.003.
6. Dong H, Li M, Zhang S et al. Short-term power load forecasting based on kernel principal component analysis and extreme learning machine. J. Electron. Meas. Instrum 2018; 32: 188-193.
7. Dukalski P, Będkowski B, Parczewski K et al. Dynamics of the vehicle rear suspension system with electric motors mounted in wheels. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(1): 125-136, https://doi.org/10.17531/ein.2019.1.14.
8. El-Fergany AA, Hasanien HM. Tree-Seed Algorithm for Solving Optimal Power Flow Problem in Large-Scale Power Systems Incorporating Validations and Comparisons. Applied Soft Computing 2018; 64: 307-316, https://doi.org/10.1016/j.asoc.2017.12.026.
9. Ertugrul ÖF. Forecasting electricity load by a novel recurrent extreme learning machines approach. International Journal of Electrical Power & Energy Systems 2016; 78: 429-435, https://doi.org/10.1016/j.ijepes.2015.12.006.
10. Faris H, Mirjalili S, Aljarah I. Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme. International Journal of Machine Learning and Cybernetics 2019; 10(10): 2901-2920, https://doi.org/10.1007/s13042-018-00913-2.
11. Fushiki T. Estimation of prediction error by using K-fold cross-validation. Statistics and Computing 2011; 21(2): 137-146, https://doi.org/10.1007/s11222-009-9153-8.
12. Glowacz A, Tadeusiewicz R, Legutko S et al. Fault diagnosis of angle grinders and electric impact drills using acoustic signals. Applied Acoustics 2021; 179: 108070, https://doi.org/10.1016/j.apacoust.2021.108070.
13. Glowacz A. Fault diagnosis of electric impact drills using thermal imaging. Measurement 2021; 171: 108815, https://doi.org/10.1016/j.measurement.2020.108815.
14. Glowacz A. Ventilation diagnosis of angle grinder using thermal imaging. Sensors 2021; 21(8): 2853, https://doi.org/10.3390/s21082853.
15. Greengard S. The internet of things. MIT press: 2021, https://doi.org/10.7551/mitpress/13937.001.0001.
16. Haklı H, Uğuz H. A novel particle swarm optimization algorithm with Levy flight. Applied Soft Computing 2014; 23: 333-345, https://doi.org/10.1016/j.asoc.2014.06.034.
17. Han J, Pei J, Kamber M. Data mining: concepts and techniques. Elsevier: 2011.
18. Houimli R, Zmami M, Ben-Salha O. Short-term electric load forecasting in Tunisia using artificial neural networks. Energy Systems 2020; 11(2): 357-375, https://doi.org/10.1007/s12667-019-00324-4.
19. Hu J, Wu M, Chen X et al. A multilevel prediction model of carbon efficiency based on the differential evolution algorithm for the iron ore sintering process. IEEE Transactions on Industrial Electronics 2018; 65(11): 8778-8787, https://doi.org/10.1109/TIE.2018.2811371.
20. Huang G, Zhu Q, Siew CK. Extreme learning machine: Theory and applications. Neurocomputing 2006; 70(1): 489-501, https://doi.org/10.1016/j.neucom.2005.12.126.
21. Kang F, Liu J, Li J, Li S. Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Structural Control and Health Monitoring 2017; 24(10): e1997, https://doi.org/10.1002/stc.1997.
22. Kapłonek W, Nadolny K, Królczyk GM. The use of focus-variation microscopy for the assessment of active surfaces of a new generation of coated abrasive tools. Meas. Sci. Rev 2016; 16(2): 42-53, https://doi.org/10.1515/msr-2016-0007.
23. Kiran MS. TSA: Tree-seed algorithm for continuous optimization. Expert Systems with Applications 2015; 42(19): 6686-6698, https://doi.org/10.1016/j.eswa.2015.04.055.
24. Kochan O, Sapojnyk H, Kochan R. Temperature field control method based on neural network. 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), IEEE: 2013; 1: 21-24, https://doi.org/10.1109/IDAACS.2013.6662632.
25. Kozieł J, Przystupa K. Using the FTA method to analyze the quality of an uninterruptible power supply unitreparation UPS. Przeglad Elektrotechniczny 2019; 95(3): 37-40, https://doi.org/10.15199/48.2019.01.20.
26. Kozłowski E, Mazurkiewicz D, Sęp J, Żabiński T. The Use of Principal Component Analysis and Logistic Regression for Cutter State Identification. International Conference Innovation in Engineering, Springer: 2021: 396-405, https://doi.org/10.1007/978-3-030-78170-5_34.
27. Kozłowski E, Mazurkiewicz D, Żabiński T et al. Assessment model of cutting tool condition for real-time supervision system. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(4): 679-685, https://doi.org/10.17531/ein.2019.4.18.
28. Krolczyk G, Gajek M, Legutko S. Predicting the tool life in the dry machining of duplex stainless steel. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2013; 15: 62-65.
29. Kyriakides E, Polycarpou M. Short Term Electric Load Forecasting: A Tutorial. In Chen K, Wang L (eds): Trends in Neural Computation, Berlin, Heidelberg, Springer Berlin Heidelberg: 2007: 391-418, https://doi.org/10.1007/978-3-540-36122-0_16.
30. Li H, Zhang S, Zhang C et al. A novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification. International Journal of Remote Sensing 2017; 38(23): 6970-6992, https://doi.org/10.1080/01431161.2017.1368102.
31. Liu C, Ye C. Bat algorithm with Levy flight characteristics. CAAI Trans Intell Syst 2013; 3: 240-246.
32. Liu T, Chen R, Xiao Y, Yang J. Stream-Based Short-Term Demand Forecasting Model Using ARIMA. Applied Mechanics and Materials 2012; 220-223: 315-318, https://doi.org/10.4028/www.scientific.net/AMM.220-223.315.
33. Mao L, Jiang Y. Medium-and long-term load forecasting based on partial least squares regression analysis. Power System Technology 2008; 32(19): 71-77.
34. Mazurek PA, Michałowska J, Koziel J et al. The intensity of electromagnetic fields in the range of GSM 900, GSM 1800 DECT, UMTS, WLAN in built-up areas. Przeglad Elektrotechniczny 2018; 94(12): 202-205, https://doi.org/10.15199/48.2018.12.45.
35. Michałowska J, Józwik J. Prediction of the parameters of magnetic field of CNC machine tools. Przeglad Elektrotechniczny, 95 (1): 134-136, https://doi.org/10.15199/48.2019.01.34.
36. Muneeswaran V, Rajasekaran MP. Performance evaluation of radial basis function networks based on tree seed algorithm. 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), 2016: 1-4, https://doi.org/10.1109/ICCPCT.2016.7530267.
37. Niu D, Ji L, Tian J. Wavelet neural network embedded expert system used in short-term load forecasting. 2011 2nd IEEE International Conference on Emergency Management and Management Sciences, IEEE: 2011: 190-193.
38. Pai P, Hong W. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Conversion and Management 2005; 46(17): 2669-2688, https://doi.org/10.1016/j.enconman.2005.02.004.
39. Pedregal DJ, Young PC. Development of improved adaptive approaches to electricity demand forecasting. Journal of the Operational Research Society 2008; 59(8): 1066-1076, https://doi.org/10.1057/palgrave.jors.2602447.
40. Przystupa K, Koziel J. Analysis of the quality of uninterruptible power supply using a UPS. 2018 Applications of Electromagnetics in Modern Techniques and Medicine (PTZE), IEEE: 2018: 191-194, https://doi.org/10.1109/PTZE.2018.8503204.
41. Przystupa K. An attempt to use FMEA method for an approximate reliability assessment of machinery. ITM Web of conferences, EDP Sciences: 2017; 15: 05001, https://doi.org/10.1051/itmconf/20171505001.
42. Przystupa K. Selected methods for improving power reliability. Przegląd Elektrotechniczny 2018; 94(12): 270-273, https://doi.org/10.15199/48.2018.12.62.
43. Razfar MR, Farshbaf ZR, Haghshenas M. Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm. The International Journal of Advanced Manufacturing Technology 2011; 52(5): 487-495, https://doi.org/10.1007/s00170-010-2757-5.
44. Sachenko A, Kochan V, Turchenko V, Golovko V, Savitsky Y, Laopoulos T. Method of construction of training set for neural network that predicts drift of data acquisition module. Patent of Ukraine 50380. G06F15/18.
45. Schölkopf B, Smola A, Müller KR. Kernel principal component analysis. In Gerstner W, Germond A, Hasler M, Nicoud J-D (eds): Artificial Neural Networks - ICANN'97, Berlin, Heidelberg, Springer: 1997: 583-588, https://doi.org/10.1007/BFb0020217.
46. Senthilnath J, Das V, Omkar SN, Mani V. Clustering Using Levy Flight Cuckoo Search. Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), India, Springer: 2013: 65-75, https://doi.org/10.1007/978-81-322-1041-2_6.
47. Senthilnath J, Kulkarni S, Raghuram Dr. et al. A novel harmony search-based approach for clustering problems. International Journal of Swarm Intelligence 2016; 2(1): 66-86, https://doi.org/10.1504/IJSI.2016.077434.
48. Song W, Beshley M, Przystupa K et al. A software deep packet inspection system for network traffic analysis and anomaly detection. Sensors 2020; 20(6): 1637, https://doi.org/10.3390/s20061637.
49. Su J, Kochan O. Common mode noise rejection in measuring channels. Instruments and Experimental Techniques 2015; 58(1): 86-89, https://doi.org/10.1134/S0020441215010091.
50. Sultoni S, Abdullah AG. Real Time Facial Recognition Using Principal Component Analysis (PCA) And EmguCV. IOP Conference Series: Materials Science and Engineering 2018; 384: 012079, https://doi.org/10.1088/1757-899X/384/1/012079.
51. Sun S, Przystupa K, Wei M et al. Fast bearing fault diagnosis of rolling element using Lévy Moth-Flame optimization algorithm and Naive Bayes. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22: 730-740, https://doi.org/10.17531/ein.2020.4.17.
52. Sun W, Wang Y, Zhang C. Forecasting CO2 emissions in Hebei, China, through moth-flame optimization based on the random forest and extreme learning machine. Environmental Science and Pollution Research 2018; 25(29): 28985-28997, https://doi.org/10.1007/s11356-018-2738-z.
53. Wang B, Zhao S, Zhang S. A distributed load forecasting algorithm based on cloud computing and extreme learning machine. Power System Technology 2014; 38(2): 526-531.
54. Wang G, Lu M, Dong Y, Zhao X. Self-adaptive extreme learning machine. Neural Computing and Applications 2016; 27(2): 291-303, https://doi.org/10.1007/s00521-015-1874-3.
55. Wang J, Jiang H, Wu Y, Dong Y. Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm. Energy 2015; 81: 627-644, https://doi.org/10.1016/j.energy.2015.01.006.
56. Yamany W, Fawzy M, Tharwat A, Hassanien A E. Moth-flame optimization for training Multi-Layer Perceptrons. 2015 11th International Computer Engineering Conference (ICENCO), 2015: 267-272, https://doi.org/10.1109/ICENCO.2015.7416360.
57. Yang J, Stenzel J. Short-term load forecasting with increment regression tree. Electric Power Systems Research 2006; 76(9-10): 880-888, https://doi.org/10.1016/j.epsr.2005.11.007.
58. Yang W, Zhou Q, Tsui KL. Differential evolution-based feature selection and parameter optimisation for extreme learning machine in tool wear estimation. International Journal of Production Research 2016; 54(15): 4703-4721, https://doi.org/10.1080/00207543.2015.1111534.
59. Yang X, Deb S. Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation 2010; 1(4): 330-343, https://doi.org/10.1504/IJMMNO.2010.035430.
60. Yeromenko V, Kochan O. The conditional least squares method for thermocouples error modeling. 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), IEEE: 2013; 1: 157-162, https://doi.org/10.1109/IDAACS.2013.6662661.
61. Yu Z, Xiao L, Li H et al. Model Parameter Identification for Lithium Batteries Using the Coevolutionary Particle Swarm Optimization Method. IEEE Transactions on Industrial Electronics 2017; 64(7): 5690-5700, https://doi.org/10.1109/TIE.2017.2677319.
62. Zhang S, Ren S, Chen R et al. Short-term Power Load Forecast Based on Big Data Reduction and PCA-improved RBF Network. Acta Metrologica Sinica 2018; 39(3): 392-396.
63. Zhang Y, Yang S, Guo Z et al. Wind speed forecasting based on wavelet decomposition and wavelet neural networks optimized by the Cuckoo search algorithm. Atmospheric and Oceanic Science Letters 2019; 12(2): 107-115, https://doi.org/10.1080/16742834.2019.1569455.
64. Zhao Y, Liu J, Lyu Z, Ding Z. Structural damage identification based on residual vectors and Tree-seed algorithm. Acta Scientiarum Naturalium Universitatis Sunyatseni 2017; 56(4): 46-50.
65. Zhou P, Ang B, Poh KL. A trigonometric grey prediction approach to forecasting electricity demand. Energy 2006; 31(14): 2839-2847, https://doi.org/10.1016/j.energy.2005.12.002.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

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Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-37532a7c-37cb-41d0-96ba-7f73612663ef