Abstract
In this paper, an improved clustered adaptive neuro-fuzzy inference system (ANFIS) to forecast an hour-ahead solar radiation data for 915 h is introduced. First, we have classified the history data of solar radiation time series to decrease the input sample size using clustering methods. Three methods are used, namely, fuzzy c-means (FCM), subtractive clustering, and grid partitioning. These methods allow classifying the input data into groups; each group has similar properties that help to understand the correlation between the data and by consequence simplify the forecasting process. Second, we designed an ANFIS structure that takes both advantages of fuzzy theory to describe the uncertain phenomena of the data and artificial neural network algorithm, which has a self-learning ability. Finally, by combining clustered data and ANFIS model, an hour-ahead forecasting is achieved, and it was validated using measured data. The advantage of the proposed method is that provides the ability to use implicitly the information associated with the forecasting problem, without a priori knowledge of the relationships between the different variables solar radiation. The comparison results show that the ANFIS with FCM clustering model gives the best results with RMSE equals to 112 W/m2 and high values of FS.
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References
Azimi R, Ghayekhloo M, Ghofrani M (2016) A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting. Energy Convers Manag 118:331–344. https://doi.org/10.1016/j.enconman.2016.04.009
Badescu V, Gueymard CA, Cheval S, Oprea C, Baciu M, Dumitrescu A, Iacobescu F, Milos I, Rada C (2013) Accuracy analysis for fifty-four clear-sky solar radiation models using routine hourly global irradiance measurements in Romania. Renew Energy 55:85–103. https://doi.org/10.1016/j.renene.2012.11.037
Bas E, Egrioglu E, Aladag CH, Yolcu U (2015) Fuzzy-time-series network used to forecast linear and nonlinear time series. Appl Intell 43:343–355. https://doi.org/10.1007/s10489-015-0647-0
Belaid S, Mellit A (2016) Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate. Energy Convers Manag 118:105–118. https://doi.org/10.1016/j.enconman.2016.03.082
Benmouiza K, Cheknane A (2013) Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models. Energy Convers Manag 75:561–569. https://doi.org/10.1016/j.enconman.2013.07.003
Benmouiza K, Cheknane A (2016) Small-scale solar radiation forecasting using ARMA and nonlinear autoregressive neural network models. Theor Appl Climatol 124:945–958. https://doi.org/10.1007/s00704-015-1469-z
Benmouiza K, Tadj M, Cheknane A (2016) Classification of hourly solar radiation using fuzzy c-means algorithm for optimal stand-alone PV system sizing. Int J Electr Power Energy Syst 82:233–241. https://doi.org/10.1016/j.ijepes.2016.03.019
Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Springer US, Boston
Boata RS, Gravila P (2012) Functional fuzzy approach for forecasting daily global solar irradiation. Atmos Res 112:79–88. https://doi.org/10.1016/j.atmosres.2012.04.011
Capderou M (1986) Atlas solaire de l’algerie.tome1, Office des. Office des publications universitaires
Chen Y-S, Cheng C-H, Chiu C-L, Huang S-T (2016) A study of ANFIS-based multi-factor time series models for forecasting stock index. Appl Intell 45:277–292. https://doi.org/10.1007/s10489-016-0760-8
Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst Appl Eng Technol 2:267–278
David M, Ramahatana F, Trombe PJ, Lauret P (2016) Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models. Sol Energy 133:55–72. https://doi.org/10.1016/j.solener.2016.03.064
Diagne M, David M, Lauret P, Boland J, Schmutz N (2013) Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renew Sust Energ Rev 27:65–76. https://doi.org/10.1016/j.rser.2013.06.042
Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3:32–57. https://doi.org/10.1080/01969727308546046
Flores JJ, Graff M, Rodriguez H (2012) Evolutive design of ARMA and ANN models for time series forecasting. Renew Energy 44:225–230. https://doi.org/10.1016/j.renene.2012.01.084
Fraser A, Swinney H (1986) Independent coordinates for strange attractors from mutual information. Phys Rev A Gen Phys 33:1134–1140
Gan M, Huang Y, Ding M, Dong XP, Peng JB (2012) Testing for nonlinearity in solar radiation time series by a fast surrogate data test method. Sol Energy 86:2893–2896. https://doi.org/10.1016/j.solener.2012.04.021
Giotis AP, Giannakoglou KC (1998) An unstructured grid partitioning method based on genetic algorithms. Adv Eng Softw 29:129–138. https://doi.org/10.1016/S0965-9978(98)00014-3
Huang J, Korolkiewicz M, Agrawal M, Boland J (2013) Forecasting solar radiation on an hourly time scale using a coupled autoregressive and dynamical system (CARDS) model. Sol Energy 87:136–149. https://doi.org/10.1016/j.solener.2012.10.012
Inman RH, Pedro HTC, Coimbra CFM (2013) Solar forecasting methods for renewable energy integration. Prog Energy Combust Sci 39:535–576. https://doi.org/10.1016/j.pecs.2013.06.002
Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685. https://doi.org/10.1109/21.256541
Ji W, Chee KC (2011) Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN. Sol Energy 85:808–817. https://doi.org/10.1016/j.solener.2011.01.013
Kaplanis S, Kaplani E (2007) A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values. Renew Energy 32:1414–1425. https://doi.org/10.1016/j.renene.2006.06.014
Kashyap Y, Bansal A, Sao AK (2015) Solar radiation forecasting with multiple parameters neural networks. Renew Sust Energ Rev 49:825–835. https://doi.org/10.1016/j.rser.2015.04.077
Kennel MB, Brown R, Abarbanel HDI (1992) Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A 45:3403–3411. https://doi.org/10.1103/PhysRevA.45.3403
Klipp E, Herwig R, Kowald A, Wierling C, Lehrach H (2005) Systems biology in practice. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim
Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. 1137–1143
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1: statistics. The regents of the University of California
Mazorra Aguiar L, Pereira B, David M, Díaz F, Lauret P (2015) Use of satellite data to improve solar radiation forecasting with Bayesian artificial neural networks. Sol Energy 122:1309–1324. https://doi.org/10.1016/j.solener.2015.10.041
Mellit A (2008) Artificial intelligence technique for modelling and forecasting of solar radiation data: a review
Olatomiwa L, Mekhilef S, Shamshirband S, Petković D (2015) Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria. Renew Sust Energ Rev 51:1784–1791. https://doi.org/10.1016/j.rser.2015.05.068
Peled A, Appelbaum J (2013) Evaluation of solar radiation properties by statistical tools and wavelet analysis. Renew Energy 59:30–38. https://doi.org/10.1016/j.renene.2013.03.019
Qazi A, Fayaz H, Wadi A, Raj RG, Rahim NA, Khan WA (2015) The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review. J Clean Prod 104:1–12. https://doi.org/10.1016/j.jclepro.2015.04.041
Rand D, Young L-S (eds) (1981) Dynamical systems and turbulence, Warwick 1980. Springer Berlin Heidelberg, Berlin
Ren Y, Suganthan PN, Srikanth N (2015) Ensemble methods for wind and solar power forecasting—a state-of-the-art review. Renew Sust Energ Rev 50:82–91. https://doi.org/10.1016/j.rser.2015.04.081
Rout M, Majhi B, Majhi R, Panda G (2014) Forecasting of currency exchange rates using an adaptive ARMA model with differential evolution based training. J King Saud University 26:7–18. https://doi.org/10.1016/j.jksuci.2013.01.002
Schmidt T, Kalisch J, Lorenz E, Heinemann D (2016) Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts. Atmos Chem Phys 16:3399–3412. https://doi.org/10.5194/acp-16-3399-2016
Simon HD (1991) Partitioning of unstructured problems for parallel processing. Comput Syst Eng 2:135–148. https://doi.org/10.1016/0956-0521(91)90014-V
Suganthi L, Iniyan S, Samuel AA (2015) Applications of fuzzy logic in renewable energy systems – a review. Renew Sust Energ Rev 48:585–607. https://doi.org/10.1016/j.rser.2015.04.037
Tadj M, Benmouiza K, Cheknane A, Silvestre S (2014) Improving the performance of PV systems by faults detection using GISTEL approach. Energy Convers Manag 80:298–304. https://doi.org/10.1016/j.enconman.2014.01.030
W.M.O (1981) Meteorological aspects of the utilization of solar radiation as an energy source, illustrate. Secretariat of the World Meteorological Organization
Yager RR, Filev DP (1994) Generation of fuzzy rules by mountain clustering. J Intell Fuzzy Syst Appl Eng Technol 2:209–219
Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175. https://doi.org/10.1016/S0925-2312(01)00702-0
Zhang G, Eddy Patuwo B, Hu MY (1998) Forecasting with artificial neural networks. Int J Forecast 14:35–62. https://doi.org/10.1016/S0169-2070(97)00044-7
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Benmouiza, K., Cheknane, A. Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theor Appl Climatol 137, 31–43 (2019). https://doi.org/10.1007/s00704-018-2576-4
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DOI: https://doi.org/10.1007/s00704-018-2576-4