Forecasting Modeling and Analysis of Power Engineering in China Based on Semi-Parametric Regression Model

• Autorzy: Wang X. , Yu B. , Chen Z. , Hou L.

Warianty tytułu

PL

Analiza i modelowanie przewidujące w elektroenergetyki w Chinach – model regresji semiparametrycznej

• Języki publikacji: EN

Abstrakty

EN

Electricity consumption forecasting is considered one of the most important tasks in energy planning, and it has great significance on management decision-making for power generation organizations and power policy adjustments for governments. In this paper, we present a new semi-parametric regression model for consumption forecasting in electrical power systems. We have used the distribution function of student residuals to replace the nonparametric component of the traditional semi-parametric model, thus eliminating the effects of the residual disturbance term according to the change trend of the consumption data themselves. Then, we use differential element theory set information aggregation intervals to create a dynamic weight distribution and improve the forecasting accuracy of the prediction models. Compared with general linear models, our models make statistical inferences and can automatically regulate the boundary effect, which gives the forecast result a higher accuracy. To present a case study, we use the historical data of electricity consumption and related influential factors in China from 1981 to 2010. The simulation results show that both in the model building stage and in the testing stage for this particular case, the SPRM prediction approach proposed in this paper outperforms the other two contrast models, the MAPE of SPRM is 3.21%, much lower than the other two values 3.84% and 13.07%.

PL

W artykule opisano model regresji semiparametrycznej do przewidywania zużycia energii elektrycznej w systemach elektroenergetycznych. W celu eliminacji wywołujących zakłócenia, nieparametrycznych składowych w tradycyjnym modelu semiparametrycznym, zastosowano rozkład studenta. Wykorzystano także metodę różnicową w ustalaniu interwałów zbierania danych, analizowanych przy przewidywaniu. Działanie i skuteczność modelu zweryfikowano z wykorzystaniem prawdziwych danych z lat 1981 do 2010.

Słowa kluczowe

EN

electricity consumption; forecasting; semi-parametric regression; information aggregation intervals; dynamic weight distribution

PL

zużycie energii elektrycznej; przewidywanie; regresja semiparametryczna; interwał zbierania informacji; rozkład dynamiczny wag

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 3b
• Strony: 161--169
• Opis fizyczny: Bibliogr. 21 poz., tab., wykr.

Twórcy

autor

Wang X.

• National Key Laboratory of Process Optimization and Intelligent Decision-Making, HeFei University of Technology, Hefei, Anhui, China

autor

Yu B.

• National Key Laboratory of Process Optimization and Intelligent Decision-Making, HeFei University of Technology, Hefei, Anhui, China

autor

Chen Z.

• National Key Laboratory of Process Optimization and Intelligent Decision-Making, HeFei University of Technology, Hefei, Anhui, China

autor

Hou L.

• National Key Laboratory of Process Optimization and Intelligent Decision-Making, HeFei University of Technology, Hefei, Anhui, China

Bibliografia

• [1] Vincenzo Bianco, Oronzio Manca, Sergio Nardini, Alina A. Minea, Analysis and forecasting of nonresidential electricity consumption in Romania, Applied Energy. 87(2010), 3584-3590.
• [2] Wang XJ, Yang SL, Ding J, Wang HJ. Dynamic GM (1,1) model based on cubic spline for electricity consumption prediction in smart grid,China Communications. 7(2010), No.4, 83-88.
• [3] Diyar Akay, Mehmet Atak. Grey prediction with rolling mechanism for electricity demand forecasting of Turkey, Energy. 32(2007), 1670-1675.
• [4] Wang XJ, Shen JX, Yang SL. Application research on Gaussian orthogonal interpolation method for electricity consumption forecasting of smart grid, Power System Protection and Control.38(2010),No.21,141-145,151.
• [5] Wang XJ, Yang SL etc. Simulation of Orthogonalization Prediction Based on Grey Markov Chain for Electricity Consumption,Journal of System Simulation. 22(2010), No.10, 2253-2256.
• [6] Roula Inglesi. Aggregate electricity demand in South Africa: Conditional forecasts to 2030, Applied Energy. 87(2010),197-204.
• [7] Ching-Lai H, Marnont A, Simon W, Shanti M. Analyzing the impact of weather variables on monthly electricity deman, IEEE Trans. 20(2005), 2078-2085.
• [8] Egelioglu F, Mohamad AA, Guven H. Economic variables and electricity consumption in Northern Cyprus, Energy. 26(2001), 355-362.
• [9] Wei L, Wu J, Liu Y. Long-term electricity load forecasting based on system dynamics, Automation of Electric Power Systems. 24(2000), No. 24, 47.
• [10] Paresh Kumar Narayan, Arti Prasad. Electricity consumptionreal GDP causality nexus: Evidence from a bootstrapped causality test for 30OECD countries, Energy Policy. 36(2008), 910-918.
• [11] K. Nikolopoulos, P. Goodwin, A. Patelis, V. Assimakopoulos. Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches, European Journal of Operational Research. 180(2007), 354-368.
• [12] Abdel-Aal RE, Al-Garni AZ, Al-Nassar YN. Modeling and forecasting monthly electric energy consumption in ekstern Saudi Arabia using abductive networks, Energy. 22(1997), 911-921.
• [13] Yan YY. Climate and residential electricity consumption in Hong Kong, Energy. 23(1998), 17-20.
• [14] M.R. AlRashidi, K.M. EL-Naggar. Long term electricity load frecasting based on particle swarm optimization, Applied Energy. 87(2010), 320-326.
• [15] Nasr GE, Badr EA, Younes MR. Neural networks in forecasting electricity energy consumption: Univariate and multivariate approaches. International Journal of Energy Research. 26 (2002), 67-78.
• [16] Metaxiotis K, Kagiannas A, Askounis D, Psarras J. Artificial intelligence in short term electricity load forecasting: a state of the art survey for the researcher, Energy Conversion and Management. 44(2003), 1525-1534.
• [17] Santos PJ, Martins AG, Pires AJ, Martins JF, Mendes RV. Short term load forecast using trend information and process reconstruction, International Journal of Energy Research. 30(2006), 811-822.
• [18] Robert F. Engle, C.W.J. Granger, John Rice, Andrew Weiss. Semi-parametric estimates of the relation between weather and electricity sales, Journal of the American Statistical Association. 81(1986), 310-320.
• [19] National Bureau of Statistics of China, 60 Years of New China, China Statistics Press. 2009.
• [20] J.F.Liu, Z.L.Deng, Self-Tuning Weighted Measurement Fusion Kalman Filter for ARMA Signals with Colored Noise, Applied Mathematics & Information Sciences, 6(2012), 1-7.
• [21] Kumar Krishen, Applications of Space Technologies to Commercial Sector, Advances in Industrial Engineering and Management, 1(2012), No.1, 1-9