Functional-type Single-input-rule-modules Connected Neural Fuzzy System for Wind Speed Prediction

Chengdong Li; Li Wang; Guiqing Zhang; Huidong Wang; Fang Shang

doi:10.1109/JAS.2017.7510640

Volume 4 Issue 4

Oct. 2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(4): 751-762

Chengdong Li, Li Wang, Guiqing Zhang, Huidong Wang and Fang Shang, "Functional-type Single-input-rule-modules Connected Neural Fuzzy System for Wind Speed Prediction," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 751-762, Oct. 2017. doi: 10.1109/JAS.2017.7510640

Citation:

Chengdong Li, Li Wang, Guiqing Zhang, Huidong Wang and Fang Shang, "Functional-type Single-input-rule-modules Connected Neural Fuzzy System for Wind Speed Prediction," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 751-762, Oct. 2017. doi: 10.1109/JAS.2017.7510640

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Functional-type Single-input-rule-modules Connected Neural Fuzzy System for Wind Speed Prediction

doi: 10.1109/JAS.2017.7510640

Chengdong Li^{1
,
,},
Li Wang^1
,,
Guiqing Zhang^1
,,
Huidong Wang^2
,,
Fang Shang^1
,

1.
School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
2.
School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China

Funds:

the National Natural Science Foundation of China 61473176

the National Natural Science Foundation of China 61402260

the National Natural Science Foundation of China 61573225

the Natural Science Foundation of Shandong Province for Outstanding Young Talents in Provincial Universities ZR2015JL021

the Natural Science Foundation of Shandong Province for Outstanding Young Talents in Provincial Universities ZR2015JL003

the Open Program from the State Key Laboratory of Management and Control for Complex Systems 20140102

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Author Bio:
Chengdong Li received the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2010. He is currently an Associate Professor at the School of Information and Electrical Engineering, Shandong Jianzhu University. His research interests include data-driven modeling and control, fuzzy logic theory and applications, fuzzy neural networks, and other computational intelligence methods. His research interests also include the intelligent control methods with applications to intelligent buildings and smart homes. He has authored and co-authored over 80 papers in international journals and conferences. He has been the Program Committee Member of several international conferences and the reviewer for several international conferences and journals. (e-mail: lichengdong@sdjzu.edu.cn)

Li Wang is a master student at the School of Information and Electrical Engineering, Shandong Jianzhu University. Her research interests include intelligent systems and intelligent buildings. (e-mail: wanglisdjzu@163.com)

Guiqing Zhang is a Professor at the School of Information and Electrical Engineering, Shandong Jianzhu University. He received the Ph.D. degree from Xi'an Jiaotong University in 2002. His research interests include intelligent control methods, intelligent buildings, smart home and internet of things. (e-mail: qqzhang@sdjzu.edu.cn)

Huidong Wang is a Lecturer at the School of Management Science and Engineering, Shandong University of Finance and Economics. He received the Ph.D. degree from the Institute of Automation, Chinese Academy of Science in 2010. His research interests include intelligent systems and type-2 fuzzy logic. (e-mail: huidong.wang@ia.ac.cn)

Fang Shang received the Ph.D. degree from Shandong University, Jinan, China, in 2010. She is currently an Associate Professor at the School of Information and Electrical Engineering, Shandong Jianzhu University. Her research interests include intelligent systems and adaptive control of nonlinear systems.(e-mail: shangfang929@163.com)
Corresponding author: Chengdong Li, e-mail:lichengdong@sdjzu.edu.cn
Received Date: 2015-09-21
Accepted Date: 2016-01-19

Abstract

Abstract

Wind is one kind of clean and free renewable energy sources. Wind speed plays a pivotal role in the wind power output. However, due to the random and unstable nature of the wind, accurate prediction of wind speed is a particularly challenging task. This paper presents a novel neural fuzzy method for the hourly wind speed prediction. Firstly, a neural structure is proposed for the functional-type single-input-rule-modules (FSIRMs) connected fuzzy inference system (FIS) to combine the merits of both the FSIRMs connected FIS and the neural network. Then, in order to achieve both the smallest training errors and the smallest parameters, a least square method based parameter learning algorithm is presented for the proposed FSIRMs connected neural fuzzy system (FSIRMNFS). Further, the proposed FSIRMNFS and its parameter learning algorithm are applied to the hourly wind speed prediction. Experiments and comparisons are also made to show the effectiveness and advantages of the proposed approach. Experimental results verified that our study has presented an effective approach for the hourly wind speed prediction. The proposed approach can also be used for the prediction of wind direction, wind power and some other prediction applications in the research field of renewable energy.
- Fuzzy inference system (FIS),
- learning algorithm,
- neural fuzzy system,
- single input rule module,
- wind speed prediction

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References(36)

References

[1]	P. S. Georgilakis, "Technical challenges associated with the integration of wind power into power systems, " Renew. Sustain. Energy Rev. , vol. 12, no. 3, pp. 852-863, Apr. 2008. http://www.sciencedirect.com/science/article/pii/S1364032106001250
[2]	L. Soder, L. Hofmann, A. Orths, H. Holttinen, Y. H. Wan, and A. Tuohy, "Experience from wind integration in some high penetration areas, " IEEE Trans. Energy Convers. , vol. 22, no. 1, pp. 4-12, Mar. 2007. http://ieeexplore.ieee.org/document/4106017/
[3]	A. Tascikaraoglu and M. Uzunoglu, "A review of combined approaches for prediction of short-term wind speed and power, " Renew. Sustain. Energy Rev. , vol. 34, pp. 243-254, Jun. 2014. http://www.sciencedirect.com/science/article/pii/S1364032114001944
[4]	J. L. Torres, García A, M. De Blas, and A. De Francisco, "Forecast of hourly average wind speed with ARMA models in Navarre (Spain), " Solar Energy, vol. 79, no. 1, pp. 65-77, Jul. 2005. http://www.sciencedirect.com/science/article/pii/S0038092X04002877
[5]	E. Erdem and J. Shi, "ARMA based approaches for forecasting the tuple of wind speed and direction, " Appl. Energy, vol. 88, no. 4, pp. 1405-1414, Apr. 2011. http://www.sciencedirect.com/science/article/pii/S0306261910004332
[6]	A. Sfetsos, "A novel approach for the forecasting of mean hourly wind speed time series, " Renew. Energy, vol. 27, no. 2, pp. 163-174, Oct. 2002. http://www.sciencedirect.com/science/article/pii/S0960148101001938
[7]	R. G. Kavasseri and K. Seetharaman, "Day-ahead wind speed forecasting using f-ARIMA models, " Renew. Energy, vol. 34, no. 5, pp. 1388-1393, May 2009. http://www.sciencedirect.com/science/article/pii/S0960148108003327
[8]	K. G. Sheela and S. N. Deepa, "Neural network based hybrid computing model for wind speed prediction, " Neurocomputing, vol. 122, pp. 425-429, Dec. 2013. http://www.sciencedirect.com/science/article/pii/S0925231213006164
[9]	H. Quan, D. Srinivasan, and A. Khosravi, "Short-term load and wind power forecasting using neural network-based prediction intervals, " IEEE Trans. Neural Netw. Learn. Syst. , vol. 25, no. 2, pp. 303-315, Feb. 2014. http://ieeexplore.ieee.org/document/6581855/
[10]	Z. H. Guo, J. Wu, H. Y. Lu, and J. Z. Wang, "A case study on a hybrid wind speed forecasting method using BP neural network, " Knowl. Based Syst. , vol. 24, no. 7, pp. 1048-1056, Oct. 2011. http://www.sciencedirect.com/science/article/pii/S0950705111000852
[11]	R. Ak, O. Fink, and E. Zio, "Two machine learning approaches for short-term wind speed time-series prediction, " IEEE Trans. Neural Netw. Learn. Syst. , vol. 27, no. 8, pp. 1734-1747, Aug. 2015, doi: 10.1109/TNNLS.2015.2418739.
[12]	L. Zjavka, "Wind speed forecast correction models using polynomial neural networks, " Renew. Energy, vol. 83, pp. 998-1006, Nov. 2015. http://www.sciencedirect.com/science/article/pii/S0960148115003341
[13]	J. J. Wang, W. Y. Zhang, J. Z. Wang, T. T. Han, and L. B. Kong, "A novel hybrid approach for wind speed prediction, " Inf. Sci. , vol. 273, pp. 304-318, Jul. 2014. http://www.sciencedirect.com/science/article/pii/S0020025514002941
[14]	G. Li and J. Shi, "On comparing three artificial neural networks for wind speed forecasting, " Appl. Energy, vol. 87, no. 7, pp. 2313-2320, Jul. 2010. http://www.sciencedirect.com/science/article/pii/S0306261909005364
[15]	I. G. Damousis, M. C. Alexiadis, J. B. Theocharis, and P. S. Dokopoulos, "A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation, " IEEE Trans. Energy Convers. , vol. 19, no. 2, pp. 352-361, Jun. 2004. http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1300701
[16]	Petković D, "Adaptive neuro-fuzzy approach for estimation of wind speed distribution, " Int. J. Electr. Power Energy Syst. , vol. 73, pp. 389-392, Dec. 2015. http://www.sciencedirect.com/science/article/pii/S0142061515002446
[17]	D. Petković, S. Shamshirband, C. W. Tong, and E. T. Al-Shammari, "Generalized adaptive neuro-fuzzy based method for wind speed distribution prediction, " Flow Meas. Instrum. , vol. 43, pp. 47-52, Jun. 2015. http://www.sciencedirect.com/science/article/pii/s0955598615000278
[18]	H. Liu, H. Q. Tian, and Y. F. Li, "Comparison of new hybrid FEEMDMLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions, " Energy Convers. Manage. , vol. 89, pp. 1-11, Jan. 2015. http://www.sciencedirect.com/science/article/pii/S0196890414008619
[19]	S. An, H. Shi, Q. H. Hu, X. Q. Li, and J. W. Dang, "Fuzzy rough regression with application to wind speed prediction, " Inf. Sci. , vol. 282, pp. 388-400, Oct. 2014. http://www.sciencedirect.com/science/article/pii/S0020025514003818
[20]	J. Q. Yi, N. Yubazaki, and K. Hirota, "Upswing and stabilization control of inverted pendulum system based on the SIRMs dynamically connected fuzzy inference model, " Fuzzy Sets Syst. , vol. 122, no. 1, pp. 139-152, Aug. 2001. http://www.sciencedirect.com/science/article/pii/S016501140000049X
[21]	J. Q. Yi, N. Yubazaki, and K. Hirota, "A new fuzzy controller for stabilization of parallel-type double inverted pendulum system, " Fuzzy Sets Syst. , vol. 126, no. 1, pp. 105-119, Feb. 2002. http://www.sciencedirect.com/science/article/pii/S0165011401000288
[22]	J. Q. Yi, N. Yubazaki, and K. Hirota, "A proposal of SIRMs dynamically connected fuzzy inference model for plural input fuzzy control, " Fuzzy Sets Syst. , vol. 125, no. 1, pp. 79-82, Jan. 2002. http://www.sciencedirect.com/science/article/pii/S0165011400001354
[23]	J. Q. Yi, N. Yubazaki, and K. Hirota, "Anti-swing and positioning control of overhead traveling crane, " Inf. Sci. , vol. 155, no. 1-2, pp. 19-42, Oct. 2003. http://www.sciencedirect.com/science/article/pii/S0020025503001270
[24]	H. Seki, H. Ishii, and M. Mizumoto, "On the monotonicity of single input type fuzzy reasoning methods, " IEICE Trans. Fund. Electron. Commun. Comput. Sci. , vol. E90-A, no. 7, pp. 1462-1468, Jul. 2007. doi: 10.1093/ietfec/e90-a.7.1462
[25]	H. Seki, H. Ishii, and M. Mizumoto, "On the generalization of single input rule modules connected type fuzzy reasoning method, " IEEE Trans. Fuzzy Syst. , vol. 16, no. 5, pp. 1180-1187, Oct. 2008. http://ieeexplore.ieee.org/document/4497918/
[26]	J. Kim and J. S. Lee, "Sufficient conditions for monotonically constrained functional-type SIRMs connected fuzzy systems, " in Proc. 2010 IEEE Int. Conf. Fuzzy Systems, Barcelona, Spain, 2010, pp. 1-6.
[27]	H. Seki and M. Mizumoto, "SIRMs connected fuzzy inference method adopting emphasis and suppression, " Fuzzy Sets Syst. , vol. 215, pp. 112-126, Mar. 2013. http://dl.acm.org/citation.cfm?id=2431030
[28]	H. Seki, "Nonlinear function approximation using fuzzy functional SIRMs inference model, " in Proc. the 11th WSEAS Int. Conf. Artificial Intelligence, Knowledge Engineering and Data Bases, Cambridge, UK, 2012, pp. 201-206. http://www.researchgate.net/publication/267365806_Nonlinear_Function_Approximation_Using_Fuzzy_Functional_SIRMs_Inference_Model
[29]	H. Seki, H. Ishii, and M. Mizumoto, "Nonlinear identification and medical diagnosis system using functional-type SIRMs connected fuzzy inference method, " Int. J. Innov. Comput. Inf. Control, vol. 6, no. 11, pp. 5275-5286, Nov. 2010. http://www.researchgate.net/publication/289690317_Nonlinear_identification_and_medical_diagnosis_system_using_functional-type_sirms_connected_fuzzy_inference_method
[30]	O. Nelles, Nonlinear System Identification. Berlin Heidelberg:SpringerVerlag, 2001.
[31]	V. N. Vapnik, Statistical Learning Theory. New York:Wiley, 1998.
[32]	A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth, "Learnability and the Vapnik-Chervonenkis dimension, " J. the ACM (JACM), vol. 36, no. 4, pp. 929-965, Oct. 1989.
[33]	J. Neter, W. Wasserman, and M. H. Kutner, " Applied Linear Statistical Models. 4th ed. Chicago:Irwin, 1996.
[34]	T. Wang, H. J. Gao, and J. B. Qiu, "A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control, " IEEE Trans. Neural Netw. Learn. Syst. , vol. 27, no. 2, pp. 416-425, Feb. 2016. http://ieeexplore.ieee.org/document/7087381/
[35]	J. B. Qiu, S. X. Ding, H. J. Gao, and S. Yin, "Fuzzy-model-based reliable static output feedback Hâˆž control of nonlinear hyperbolic PDE systems, " IEEE Trans. Fuzzy Syst. , vol. 24, no. 2, pp. 388-400, Apr. 2016, doi: 10.1109/TFUZZ.2015.2457934.
[36]	J. B. Qiu, Y. L. Wei, and H. R. Karimi, "New approach to delaydependent Hâˆž control for continuous-time Markovian jump systems with time-varying delay and deficient transition descriptions, " J. Franklin Inst. , vol. 352, no. 1, pp. 189-215, Jan. 2015.

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Functional-type Single-input-rule-modules Connected Neural Fuzzy System for Wind Speed Prediction

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