Abstract
The average generation of electricity is getting increased day by day due to its increasing demand. So forecasting the future needs of electricity is very essential, especially in India. In this paper, a Grey Model (GM) and a Nonlinear Grey Model (NGM) are introduced with the concept of the Bernoulli Differential Equation (BDE) to obtain higher predictive precision, accuracy rate. To improve the prediction accuracy of GM, the Nonlinear Grey Bernoulli Model (NGBM) is used. The NGBM model is having the capability to produce more reliable outcomes. The NGBM with power r is a nonlinear differential equation. Using power r in NGBM the expected result can be controlled and adjusted to fit the results of 1-AGO historical raw data. NGBM is a recent grey prediction model to easily adjust for the correctness of GM(1, 1) stable with a BDE. The differentiation of desired outcome with the actual GM(1, 1) has been displayed through a feasible forecasting model NGBM(1, 1) by accumulating the decisive variables. This model may help government to extend future planning for generation of electricity.
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References
Deng JL Introduction of Grey system. Journal of Grey System 1 (1989): 1–24.
Wang YF Predicting stock price using fuzzy Grey prediction system, Expert Systems with Applications 22 (2002): 33–38.
Yong H A new forecasting model for agricultural commodities. Journal of Agricultural Engineering Research 60 (1995): 227–235.
Xu QY, Wen YH. The application of Grey model on the forecast of passenger of international air transportation. Transportation Planning Journal 26 (1997): 525–555.
Bahrami S, Hooshmand RA, Parastegari M Short term electric load forecasting by wavelet transform and grey model improved by PSO algorithm. Energy 72 (2014): 434–442.
Chang SC, Lai HC, Yu HC A variable P value rolling Grey forecasting model for Taiwan semiconductor industry generation. Technological Forecasting and Social Change 72 (2005): 623–640.
J.L. Deng, Control problems of grey systems, System. Control Letters, volume 5, (1982) 288–294.
M.S. Yin, Fifteen years of grey system theory research: a historical review and bibliometric analysis Application. 40(7) (2013) 2767–2775.
Sifeng Liu, Yi Lin, “Grey Systems Modelling” Grey Information: Theory and Practical, Springer-Verlag London Limited (2006) 1610–3947.
E. Kayacan, B. Ulutas, O. Kaynak, Grey system theory-based models in time series prediction, Expert System. Application. 37 (2010) 1784–1789.
Y. Peng, M. Dong, A hybrid approach of HMM and grey model for age dependent health prediction of engineering assets, Expert System. Appl. 38 (2011) 12946–12953.
Y.H.L in, P. C. Lee, Novel high-precision grey forecasting model, Autom. Constr. 16 (2007) 771–777.
C. X. Fan, S. Q. Liu, Wind speed forecasting method: grey related weighted combination with revised parameter, Energy Procardia 5 (2011) 550–554.
V. Bianco, O. Manca, S. Nardini, A. A. Minea, Analysis and forecasting of nonresidential electricity consumption in Romania, Appl. Energy 87 (2010) 3584–3590.
M.L. Lei, Z. R. Feng, A proposed grey model for short term electricity price forecasting in competitive power markets, Int. J. Electr. Power Energy Syst. 43 (2012) 531–538.
C.S. Lin, F. M. Liou, C. P. Huang, Grey forecasting model for CO2 emissions: a Taiwan study, Appl. Energy 88 (2011) 3816–3820.
C.I. Chen, H. L. Chen, S. P. Chen, Forecasting of foreign exchange rates of Taiwans major trading partners by novel nonlinear Grey Bernoulli model NGBM(1, 1), Common. Nonlinear Sci. Number. Simul 13 (2008) 1194–1204.
Hsu, L. C. Applying the Grey prediction model to the global integrated circuit industry. Technological Forecasting and social change 70 (2003), 567–574.
Dang, Y.G., Liu, S.F., Liu, B., The GM models that x (1)(n) be taken as initial value. Chin. J. Manag. Sci. 13 (1) (2005), 132–135.
J. Zhou, R. Fang, Y. Li, Y. Zhang, and B. Peng, “Parameter optimization of nonlinear grey Bernoulli model using particle swarm optimization,” Applied Mathematics and Computation, vol. 207, no. 2, pp (2009) 292–299.
Xu, T., Leng, S.X., Improvement and application of initial values of grey system model. J. Shandong Inst. Technol. 13 (1) (1999), 15–19.
Liu, B., Liu, S.F., Zhai, Z.J., Dang, Y.G., Optimum time response sequence for GM(1, 1). Chin. J. Manag. Sci. 11 (4) (2003), 54–57.
Zhang, D.H., Jiang, S.F., Shi, K.Q., Theoretical defect of grey prediction formula and its improvement. Syst. Eng. Theory Pract. 22 (8) (2002), 140–142.
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Singh, D.P., Gadakh, P.J., Dhanrao, P.M., Mohanty, S., Swain, D., Swain, D. (2017). An Application of NGBM for Forecasting Indian Electricity Power Generation. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-10-3874-7_20
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DOI: https://doi.org/10.1007/978-981-10-3874-7_20
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