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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Systems Engineering Procedia, Volume 5, 2012, Pages 179–184
The demand for energy supply has been increasing dramatically in recent years in the global. In addition, owing to the uncertain economic structure of the county, energy has a chaotic and nonlinear trend. In this paper, An improved grey G(1,1) prediction model is proposed to the energy management engineering. It is one approach that can be used to construct a model with limited samples to provide better forecasting advantage for long-term problems. The forecasting performance of the improved GM(1,1) model has been confirmed using the China's energy database. And the results, compared with those from artificial neural network (ANN) and times series. According to the experimental results, our proposed new method obviously can improve the prediction accuracy of the original grey model.
Recently the global economic recession leads to the fluctuation of energy prices, which has made energy supply to be increasingly unstable. Therefore, in order to take the way of sustainable development, it has the vital practical significance to complete the analysis of energy supply and demand gap forecast, and provide the decision data for the establishment of energy management engineering. Multivariate modeling along with co-integrated techniques or regression analysis has been used in a number of studies to analyze and forecast energy consumption [1-4]. Recently, grey forecasting approach has gained popularity in energy demand forecasting. Zhou et al.  presented a univariate trigonometric grey predictive model for forecasting electricity demand in China. This method constructs residual series into generalized trigonometric model to increase the accuracy of GM(1,1) model. Akay et al.  observed that there are chaotic phenomenon and nonlinear trend in historical electricity consumption data. It applied a method combining the grey prediction model with rolling mechanism, which is applicable for prediction with high accuracy, but limited to case with limited data or little calculation effort. Wang et al. [7-9] proposed the reconstruction of background value using interpolation algorithm, it improve prediction accuracy of models to some extent.The aim of this paper is to focus on forecasts for China’s energy management engineering using the combinative interpolation Grey predictive modeling. The rest of this paper is organized as follows. In Section 2, the presentenergy situation of China is described. Section 3 presents the conventional GM(1,1) model, and proposes combination interpolation method to reconstruct GM(1,1) model. The application of improved GM(1,1) model and model comparisons are explained in Section 4.The last section summarizes and proposes related solving suggestions for energy supply system engineering.
نتیجه گیری انگلیسی
Forecasting trends in the energy management engineering using empirical methods is very difficult, because the energy supply is strongly affected by economic cycles environmental changes. Consequently, the issue of how to obtain an accurate forecast is very important. The GM(1,1) model not only requires minimal data but also is the best among all existing models at long-term foresting. This work only examines forecasting models to determine which has better- accuracy prediction results, and numerous related influences each other in the energy management engineering. Grey relational analysis can be applied to determine relationships among these influences, an area that should be researched further in the future.