Abstract
The global priority in the production of clean and sustainable energy necessitates a reliable method of generation forecasts. However, the stochastic nature of renewable energy resources develops uncertainty in the decision-making processes of energy markets, which are not addressed in the traditional deterministic forecasts. This paper proposes a two-step process to implement a data-driven deterministic forecast-based probabilistic method for the potential quantification of uncertainty involved in wind power generation forecasts. In the first step, deterministic forecast approach integrates variational mode decomposition (VMD), discrete wavelet transforms (DWT) and Autoregressive integrated moving average (ARIMA) that creates a new combination of hybrid method for an effective short-term wind power forecast. The second step employs Gauss–Newton regression method to create an appropriate confidence interval (CI) and prediction intervals (PI) to assess the market risk allied with the uncertainty in wind power deterministic forecasts. CIs and PIs for the various confidence levels of 95%, 90% and 70% are constructed on the outcomes of the deterministic forecast using the Gauss–Newton regression method. Thereby the proposed two-step process approach presents a novel method for short-term wind power forecasts together with the uncertainty related to the forecasts. Testing with the historical wind speed data from two wind sites shows that VMD-DWT-ARIMA outperforms all other comparison models and Gauss–Newton regression method produces more rational CIs and PIs than the state-of-the-art methods.
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References
Jung, J.; Broadwater, R.P.: Current status and future advances for wind speed and power forecasting. Renew. Sustain. Energy Rev. 31(C), 762–777 (2014)
Firat, U.; Engin, S.N.; Saraclar, M.; et al.: Wind speed forecasting based on second order blind identification and autoregressive model. In: 9th Int. Conf. on machine learning and applications, pp. 686–691 (2010)
Erdem, E.; Shi, J.: ARMA based approaches for forecasting the tuple of wind speed and direction. Energy 88(4), 1405–1414 (2011)
Vishnupriyadharshini, A.; Vanitha, V.; Palanisamy, T.: Wind speed forecasting based on statistical Auto Regressive Integrated Moving Average (ARIMA) method. Int. J. Control Theory Appl. 9(15), 7681–7690 (2016)
Krishnaveny R. N.; Vanitha, V.; Jisma, M.: Forecasting of wind speed using ANN, ARIMA and Hybrid Models. In: IEEE Int. Conf. on Intelligent Computing, Instrumentation and Control Technologies, pp. 170–175 (2017)
Ahmet, A.S.; Hacer, G.G.: Forecasting the biomass-based energy potential using artificial intelligence and geographic information systems: a case study. Eng. Sci. Technol. Int. J. 26, 100992 (2021)
Zhao, J.; Guo, Z.-H.; Su, Z.-Y.; Zhao, Z.-Y.; Xia, X.; Feng, L.: An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed. Appl. Energy 162, 808–826 (2016)
Li, G.; Shi, J.: On comparing three artificial neural networks for wind speed forecasting. Appl. Energy 87(7), 2313–2320 (2010)
Hervás-Martínez, C.; Gutiérrez, P.A.; Fernández, J.C.; et al.: Hyperbolic tangent basis function neural networks training by hybrid evolutionary programming for accurate short-term wind speed prediction. In: Proc. 9th Int. Sys. Design and Al. Conf., pp. 193–198 (2009)
Salcedo-sanz, S.; Pastor, A.; Prieto, L., et al.: Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization—extreme learning machine aroach. Energy Convers. Manag. 87, 10–18 (2014)
Salcedo-sanz, S.; Pastor, A.; Ser, J.D., et al.: A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction. Renew. Energy 75, 93–101 (2015)
Zhang, C.; Wei, H.; Zhao, J.; Liu, T.; Zhu, T.; Zhang, K.: Short-term wind speed forecasting using empirical mode decomposition and feature selection. Renew. Energy 96, 727–737 (2016)
Mohammadi, K.; Shamshirband, S.; Tong, C.W.; Arif, M.; Petkovic, D.; Sudheer, Ch.: A new hybrid support vector machine-wavelet transform approach for estimation of horizontal global solar radiation. Energy Conver. Manag. 92, 162–171 (2015)
Wang, J.; Zhang, W.; Li, Y.; Wang, J.; Dang, Z.: Forecasting wind speed using empirical mode decomposition and elman neural network. Appl. Soft Comput. 23, 452–459 (2014)
Ye, L.; Liu, P.: Combined model based on EMD-SVM for short-term wind power prediction. Int. Proc. CSEE 31(31), 102–108 (2011)
Liu, H.; Tian, H.; Pan, D.; Li, Y.: Forecasting models for wind speed using wavelet, wavelet packet, time series and artificial neural networks. Appl. Energy 107, 191–208 (2013)
Gilles, J.: Empirical wavelet transform. IEEE Trans. Signal Process. 61(16), 3999–4010 (2013)
Wu, Z.; Huang, N.E.: Ensemble empirical mode decomposition; a noise-assisted data analysis method. Adv. Adapt. Data Anal. 01(1), 1–41 (2009)
Hufang, Y.; Zaiping, J.; Haiyan, L.: A hybrid wind speed forecasting system based on a ‘decomposition and ensemble’ strategy and fuzzy time series. Energies 10(9), 1–30 (2017)
Vanitha, V.; Raphel, D.; Resmi, R.: Forecasting of wind power using variational mode decomposition-adaptive neuro fuzzy inference system. Innov. Power Adv. Comput. Technol. 1, 1–4 (2019)
Ali, M.; Khan, A.; Rehman, N.: Hybrid multiscale wind speed forecasting based on variational mode decomposition. Int. Trans Electr. Energy Syst. 28, 1–21 (2018)
Deyun, W.; Hongyuan, L.; Olivier, G.; Yanbing, L.: Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction. Renew. Energy 113, 1345–1358 (2017)
Fu T: A hybrid prediction of wind speed based on variational mode decomposition method and long short-term memory. In: Int. Conf. Computer Engg. and Appl. (ICCEA), pp. 408–412 (2020)
Bo, H.; Niu, X.; Wang, J.: Wind speed forecasting system based on the variational mode decomposition strategy and immune selection multi-objective dragonfly optimization algorithm. IEEE Access 7, 178063–178081 (2019)
Zhang, G.; Xu, B.; Liu, H.; Hou, J.; Zhang, J.: Wind power prediction based on variational mode decomposition and feature selection. J. Modern Power Syst. Clean Energy 9(6), 1520–1529 (2021)
Hui, L.; Xiwei, M.; Yanfei, L.: Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM. Energy Conv. Manage. 159, 54–64 (2018)
Yu-Xi, W.; Qing-Biao, W.; Jia-Qi, Z.: Data-driven wind speed forecasting using deep feature extraction and LSTM. IET Ren. Gen. 13(12), 2062–2069 (2019)
Chun-Yang, Z., et al.: Predictive deep Boltzmann machine for multiperiod wind speed forecasting. IEEE Trans. Sust. Energy 6(4), 1416–1425 (2015)
Haque, A.U.; Nehrir, M.H.; Mandal, P.: A hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting. IEEE Trans. Power Syst. 29(4), 1663–1672 (2014)
Kirthika, N.; Ramachandran, K. I.; Sasi, K. Kottayil.: Deep quantile regression based wind generation and demand forecasts. In: Proc. of 11th Int. Conf. on Soft Computing and Pattern recognition, pp. 112–122 (2021)
Errouissi, R.; Cardenas-Barrera, J.; Meng, J.; et al.: Bootstrap prediction interval estimation for wind speed forecasting. In: IEEE Energy Convers. Congr. Expo. ECCE, pp. 1919–1924 (2015)
Hong, Y.; Jinsha, Y.; Tiefeng, Z.: A model and algorithm for the minimum probability interval of wind power forecasting error based on Beta distribution. Proc. CSEE 35(9), 2135–2142 (2015)
Jinhua, Z.; Jie, Y.; David, I., et al.: Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model. Appl. Energy 241, 229–244 (2019)
Qin, L.; Jun, W.; Weiting, Q.: Denoising of wind speed data by wavelet thresholding. In IEEE Chinese Automation Congress, pp. 518–521 (2013)
Hu, J.; Wang, J.; Zeng, G.: A hybrid forecasting approach applied to wind speed time series. Renew. Energy 60(1), 185–194 (2013)
Saida, M.; Gobind, G. P.: Wind speed and wind power forecasting using wavelet denoising-GMDH neural network. In 5th Int. Conf. on Electrical Engineering Boumerdes (2017)
Zhao, Y.; Yue, Y.; Huang, J., et al.: CEEMD and wavelet transform jointed de-noising method. Progress Geophys. (in Chin.) 30(6), 2870–2877 (2015)
Shuai, W.; Biao, Z.; Hong-Bin, S.: Objective resolution measurement in single particle reconstructions based on a new spectral signal-to-noise ratio estimation. In: Chinese Control and Decision Conf., pp. 2067–2072 (2016)
Bates and Watts: Nonlinear Regression Analysis and Its Applications. Wiley, Hoboken (1998)
Anne, K.; Peter, L.B.; Jasper, D., et al.: Confidence and Prediction Intervals For Pharmacometric Models. CPT Pharmacometr. Syst. Pharmacol. 7(6), 360–373 (2018)
Gamesa - wind turbine datasheet: https://en.wind-turbinemodels.com/turbines/428gamesa-g114-2.0mw (2020)
Vaishali, S.; Gupta, S.C.; Nema, R.K.: A critical review on wind turbine power curve modelling techniques and their applications in wind based energy systems. J. Energy 2016, 1–18 (2016)
Ren, Y.; Suganthan, P.; Srikanth, N.: A comparative study of empirical mode decomposition-based short-term wind speed forecasting methods. IEEE Trans. Sustain. Energy 6(1), 236–244 (2015)
Pinson, P.; Nielsen, H.A.; Møller, J.K., et al.: Nonparametric probabilistic forecasts of wind power; required properties and evaluation. Wind Energy 10, 497–516 (2017)
Khosravi, A.; Nahavandi, S.; Creighton, D., et al.: Comprehensive review of neural ne work-based prediction intervals and new advances. IEEE Trans. Neural Netw. 22(9), 1341–1356 (2011)
NIWE: http://niwe.res.in/NIWE_WRA_DATA/ (2020)
Central Electricity Regulatory Commission orders: http://www.cercind.gov.in/2017/orders/187N.pdf (2020)
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Kirthika, N., Ramachandran, K.I. & Kottayil, S.K. A Data-Driven Deterministic Forecast-Based Probabilistic Method for Uncertainty Estimation of Wind Power Generation. Arab J Sci Eng 47, 14147–14162 (2022). https://doi.org/10.1007/s13369-022-06683-y
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DOI: https://doi.org/10.1007/s13369-022-06683-y