Abstract
This study investigates the influence of wavelet decomposition on standalone adaptive neurofuzzy inference system (ANFIS) and its hybrid with evolutionary algorithms [genetic algorithm (GA) and particle swarm optimization (PSO)] at a medium-term temporal scale. Time series data of global horizontal irradiance used as inputs was decomposed according to 5-level wavelet sub-signals and modeled independently. Statistical metrics were used to evaluate the models, and these results were compared for all models. From our findings, the wavelet-based models incurred more computational time and lesser accuracy compared to the ANFIS and its hybrid models. ANFIS-GA and its wavelet hybrid had a high computational time of 7 s and 15.2 s, respectively., Among the six models, the standalone ANFIS model outperformed its hybrids with a root mean square error (RMSE) of 13.07, normalized root mean square error (NRMSE) of 0.11%, mean absolute percentage error (MAPE) of 4.57%, variance accounted for (VAF) of 92.03%, relative coefficient of variation (RCoV) of 0.24, and computational time (CT) of 3 s. The integration of wavelet decomposition reduced the performance of the standalone ANFIS and its hybrids with wavelet-based PSO-ANFIS recording the least model accuracy (RMSE = 31.02, NRMSE = 0.19, MAPE = 15.31, VAF = 55.89, RCoV = 0.21, CT = 8.4 s). Overall, standalone machine learning models could be more efficient, computationally less intensive, and computationally less costly than their hybrids if their hyperparameters are appropriately tuned.
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Data availability
The datasets generated during the current study are available from the corresponding author on reasonable request.
References
Abdella M, Marwala T (2005) The use of genetic algorithms and neural networks to approximate missing data in database. Comput Inform 24:577–589
Abdullah NA, Koohi-Kamali S, Abd. Rahim N (2018) Forecasting of solar radiation in Malaysia using the artificial neural network and wavelet transform. In: IET conference publications, 2018(CP749). https://doi.org/10.1049/cp.2018.1303
Adedeji PA, Akinlabi SA, Madushele N, Olatunji OO (2020a) Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: a mini review. J Clean Prod 269:1–29
Adedeji PA, Akinlabi SA, Olatunji OO (2020b) Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation. Int J Energy Res. https://doi.org/10.1002/er.5620
Adedeji PA, Akinlabi S, Madushele N, Obafemi O (2020c) Hybrid adaptive neuro-fuzzy inference system (ANFIS) for a multi-campus university energy consumption forecast. Int J Ambient Energy. https://doi.org/10.1080/01430750.2020.1719885
Adedeji PA, Olatunji OO, Madushele N, Ajayeoba AO (2021) Soft computing in renewable energy system modeling. In: Design, analysis, and applications of renewable energy systems. INC. https://doi.org/10.1016/b978-0-12-824555-2.00026-5
Adedeji PA, Olatunji OO, Madushele N, Jen TC (2021) Evolutionary-based hyperparameter tuning in machine learning models for condition monitoring in wind turbines—a survey. In: Proceedings of 2021 IEEE 12th international conference on mechanical and intelligent manufacturing technologies, ICMIMT 2021, pp 254–258. https://doi.org/10.1109/ICMIMT52186.2021.9476200
Aliyu AK, Modu B, Tan CW (2018) A review of renewable energy development in Africa: a focus in South Africa, Egypt and Nigeria. Renew Sustain Energy Rev 81:2502–2518. https://doi.org/10.1016/j.rser.2017.06.055
Babu MK, Ray P (2019) A wavelet neural network model for hourly solar radiation forecasting from daily solar radiation. In: 2019 IEEE 5th international conference for convergence in technology, I2CT 2019, pp 1–5. https://doi.org/10.1109/I2CT45611.2019.9033864
Benmouiza K, Cheknane A (2019) Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theor Appl Climatol 137:31–43. https://doi.org/10.1007/s00704-018-2576-4
Chauvin R, Nou J, Thil S, Grieu S (2014) Intra-day DNI forecasting under clear sky conditions using ANFIS. In: IFAC proceedings volumes (IFAC-PapersOnline), vol 19, Issue 3. IFAC. https://doi.org/10.3182/20140824-6-za-1003.02087
Colak I, Yesilbudak M, Genc N, Bayindir R (2016) Multi-period prediction of solar radiation using ARMA and ARIMA models. In: Proceedings—2015 IEEE 14th international conference on machine learning and applications, ICMLA 2015, pp 1045–1049. https://doi.org/10.1109/ICMLA.2015.33
Cozzi L (2012) World Energy Outlook 2012 (Issue November)
Deo RC, Wen X, Qi F (2016) A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset. Appl Energy 168:568–593. https://doi.org/10.1016/j.apenergy.2016.01.130
DoE (2018) 2018 South African Energy Sector Report. Department of Energy, South Africa. http://www.energy.gov.za/files/media/explained/2018-South-African-Energy-Sector-Report.pdf
Dong Z, Yang D, Reindl T, Walsh WM (2015) A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance. Energy 82:570–577. https://doi.org/10.1016/j.energy.2015.01.066
Douiri MR (2019) Particle swarm optimized neuro-fuzzy system for photovoltaic power forecasting model. Sol Energy 184(March):91–104. https://doi.org/10.1016/j.solener.2019.03.098
Engelbrecht AP (2007) Computational intelligence: an introduction. In: Computational intelligence: an introduction
Ghimire S, Deo RC, Raj N, Mi J (2019) Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction. Renew Sustain Energy Rev 113(July):109247. https://doi.org/10.1016/j.rser.2019.109247
Guermoui M, Rabehi A (2018) Soft computing for solar radiation potential assessment in Algeria. Int J Ambient Energy. https://doi.org/10.1080/01430750.2018.1517686
Halabi LM, Mekhilef S, Hossain M (2018) Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation. Appl Energy 213(January):247–261. https://doi.org/10.1016/j.apenergy.2018.01.035
Hossain M, Mekhilef S, Afifi F, Halabi LM, Olatomiwa L, Seyedmahmoudian M, Horan B, Stojcevski A (2018) Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability. PLoS One 13(4):1–31
Housny H, Chater EA, Fadil HE (2020) PSO-based ANFIS for quadrotor system trajectory-tracking control. In: 2020 1st international conference on innovative research in applied science, engineering and technology, IRASET 2020, vol 1, pp 0–5. https://doi.org/10.1109/IRASET48871.2020.9092015
Huang X, Shi J, Gao B, Tai Y, Chen Z, Zhang J (2019) Forecasting hourly solar irradiance using hybrid wavelet transformation and elman model in smart grid. IEEE Access 7:139909–139923. https://doi.org/10.1109/ACCESS.2019.2943886
Hussain S, AlAlili A (2017) A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks. Appl Energy 208(May):540–550. https://doi.org/10.1016/j.apenergy.2017.09.100
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541
Kabir E, Kumar P, Kumar S, Adelodun AA, Kim KH (2018) Solar energy: potential and future prospects. Renew Sustain Energy Rev 82(September 2017):894–900. https://doi.org/10.1016/j.rser.2017.09.094
Kavasseri RG, Seetharaman K (2009) Day-ahead wind speed forecasting using f-ARIMA models. Renew Energy 34(5):1388–1393. https://doi.org/10.1016/j.renene.2008.09.006
Khelifi R, Guermoui M, Rabehi A, Lalmi D (2020) Multi-step-ahead forecasting of daily solar radiation components in the Saharan climate. Int J Ambient Energy 41(6):707–715. https://doi.org/10.1080/01430750.2018.1490349
Khokhar S, Asuhaimi A, Zin M, Pervez A, Safawi A (2017) A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network. Measurement 95:246–259. https://doi.org/10.1016/j.measurement.2016.10.013
Kumar KR, Kalavathi MS (2018) Artificial intelligence based forecast models for predicting solar power generation. Mater Today Proc 5(1):796–802. https://doi.org/10.1016/j.matpr.2017.11.149
Kushwaha V, Pindoriya NM (2019) A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast. Renew Energy 140:124–139. https://doi.org/10.1016/j.renene.2019.03.020
Maistry N, McKay TM (2016) Promoting energy efficiency in a South African university. J Energy Southern Afr 27(3):1–10
Mandal P, Teja S, Madhira S, Ul A, Meng J (2012) Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques. Proc Comput Sci 12(915):332–337. https://doi.org/10.1016/j.procs.2012.09.080
McEwan C (2017) Spatial processes and politics of renewable energy transition: land, zones and frictions in South Africa. Polit Geogr 56:1–12. https://doi.org/10.1016/j.polgeo.2016.10.001
Mellit A, Arab AH, Khorissi N (2007) An ANFIS-based forecasting for solar radiation data from sunshine duration and ambient temperature. IEEE Power Eng Soc Gen Meet 2007:1–6
Mohammadi K, Shamshirband S, Anisi MH, Amjad Alam K, Petković D (2015) Support vector regression based prediction of global solar radiation on a horizontal surface. Energy Convers Manag 91:433–441. https://doi.org/10.1016/j.enconman.2014.12.015
Mottahedi A, Sereshki F, Ataei M (2018) Overbreak prediction in underground excavations using hybrid ANFIS-PSO model. Tunn Undergr Space Technol 80(April 2017):1–9. https://doi.org/10.1016/j.tust.2018.05.023
Muhammad A, Gaya MS, Aliyu R, Abdulkadir A (2018) Forecasting of global solar radiation using anfis and armax techniques. In: IOP Conf. Series: materials science and engineering, pp 1–7. https://doi.org/10.1088/1757-899X/303/1/012016
Olatomiwa L, Mekhilef S, Shamshirband S, Mohammadi K, Petković D, Sudheer C (2015a) A support vector machine-firefly algorithm-based model for global solar radiation prediction. Sol Energy 115:632–644. https://doi.org/10.1016/j.solener.2015.03.015
Olatomiwa L, Mekhilef S, Shamshirband S, Petkovi D (2015b) Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria. Renew Sustain Energy Rev 51:1784–1791. https://doi.org/10.1016/j.rser.2015.05.068
Olatunji O, Akinlabi S, Nkosinathi M, Adedeji PA (2019) Estimation of the elemental composition of biomass using hybrid adaptive neuro-fuzzy inference system. Bioenergy Res 12(3):642–652
Pavlova ON, Guyo GA, Pavlov AN (2022) Multiresolution wavelet analysis of noisy datasets with different measures for decomposition coefficients. Phys A 585:126406. https://doi.org/10.1016/j.physa.2021.126406
Perez R, Lauret P, Perez M, David M, Hoff TE, Kivalov S (2018) Solar resource variability. In: Wind field and solar radiation characterization and forecasting: a numerical approach for complex terrain. Springer International Publishing AG, New York, pp 149–170. https://doi.org/10.1007/978-3-319-76876-2_7
Perveen G, Rizwan M, Goel N (2019) An ANFIS-based model for solar energy forecasting and its smart grid application. Eng Rep. https://doi.org/10.1002/eng2.12070
Pousinho HMI, Catalão JPS, Mendes VMF (2010) Wind power short-term prediction by a hybrid PSO-ANFIS approach. Proc Mediterr Electrotech Conf MELECON. https://doi.org/10.1109/MELCON.2010.5475923
Power HC, Mills DM (2005) Solar radiation climate change over southern Africa and an assessment of the radiative impact of volcanic eruptions. Int J Climatol 25(3):295–318. https://doi.org/10.1002/joc.1134
Ravikumar S, Kavitha D (2021) IOT based autonomous car driver scheme based on ANFIS and black widow optimization. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02725-1
Reza Parsaei M, Mollashahi H, Darvishan A, Mir M, Simoes R (2020) A new prediction model of solar radiation based on the neuro-fuzzy model. Int J Ambient Energy 41(2):189–197. https://doi.org/10.1080/01430750.2018.1456964
Risse M (2019) Combining wavelet decomposition with machine learning to forecast gold returns. Int J Forecast 35(2):601–615. https://doi.org/10.1016/j.ijforecast.2018.11.008
Salisu S, Mustafa MW, Mustapha M, Mohammed OO (2019a) A hybrid PSO-ANFIS approach for horizontal solar radiation prediction in Nigeria. ELEKTRIKA J Electr Eng 18(2):23–32. https://doi.org/10.11113/elektrika.v18n2.153
Salisu S, Mustafa MW, Mustapha M, Mohammed OO (2019b) Solar radiation forecasting in Nigeria based on hybrid PSO-ANFIS and WT-ANFIS approach. Int J Electr Comput Eng 9(5):3916–3926. https://doi.org/10.11591/ijece.v9i5.pp3916-3926
Semero YK, Zheng D, Zhang J (2018) A PSO-ANFIS based hybrid approach for short term PV power prediction in microgrids. Electr Power Compon Syst 46(1):1–9. https://doi.org/10.1080/15325008.2018.1433733
Senthilselvi A, Duela JS, Prabavathi R, Sara D (2021) Performance evaluation of adaptive neuro fuzzy system (ANFIS) over fuzzy inference system (FIS) with optimization algorithm in de-noising of images from salt and pepper noise. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03024-z
Seo Y, Kim S, Kisi O, Singh VP (2015) Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. J Hydrol 520:224–243. https://doi.org/10.1016/j.jhydrol.2014.11.050
Sridharan M (2021) Application of Mamdani fuzzy inference system in predicting the thermal performance of solar distillation still. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02810-5
Suleymani M, Bemani A (2018) Application of ANFIS-PSO algorithm as a novel method for estimation of higher heating value of biomass. Energy Sources Part A Recovery, Util Environ Effects 40(3):288–293. https://doi.org/10.1080/15567036.2017.1413453
Suparta W, Alhasa KM (2016) Adaptive neuro-fuzzy inference system. In: Modeling of tropospheric delays using ANFIS, Issue 2009, pp 5–19. https://doi.org/10.1007/978-3-319-28437-8
Wang J, Wang Z, Li J, Wu J (2018) Multilevel wavelet decomposition network for interpretable time series analysis. Proc ACM SIGKDD Int Conf Knowl Discov Data Min. https://doi.org/10.1145/3219819.3220060
Zhu H, Li X, Sun Q, Nie L, Yao J, Zhao G (2016) A power prediction method for photovoltaic power plant based on wavelet decomposition and artificial neural networks. Energies 9(1):1–15. https://doi.org/10.3390/en9010011
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Adedeji, P.A., Akinlabi, S.A., Madushele, N. et al. Evolutionary-based neurofuzzy model with wavelet decomposition for global horizontal irradiance medium-term prediction. J Ambient Intell Human Comput 14, 9793–9805 (2023). https://doi.org/10.1007/s12652-021-03639-2
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DOI: https://doi.org/10.1007/s12652-021-03639-2