Electricity demand forecasting plays an important role in smart grid expansion planning.In this paper,we present a dynamic GM(1,1) model based on grey system theory and cubic spline function interpolation principle.Us...Electricity demand forecasting plays an important role in smart grid expansion planning.In this paper,we present a dynamic GM(1,1) model based on grey system theory and cubic spline function interpolation principle.Using piecewise polynomial interpolation thought,this model can dynamically predict the general trend of time series data.Combined with low-order polynomial,the cubic spline interpolation has smaller error,avoids the Runge phenomenon of high-order polynomial,and has better approximation effect.Meanwhile,prediction is implemented with the newest information according to the rolling and feedback mechanism and fluctuating error is controlled well to improve prediction accuracy in time-varying environment.Case study using the living electricity consumption data of Jiangsu province in 2008 is presented to demonstrate the effectiveness of the proposed model.展开更多
Grey system theory has been widely applied to many domains such as economy, agriculture, management, Social Sciences and so on. Based on the theory of grey system, this paper established GM（1,1） grey predict model f...Grey system theory has been widely applied to many domains such as economy, agriculture, management, Social Sciences and so on. Based on the theory of grey system, this paper established GM（1,1） grey predict model for the first time to forecast The number of Scitech novelty search item and The staff number of Sci-Tech Novelty Search. The predicting results are almost close to the actual values, which shows that the model is reliable so that the models could be used to forecast the two factors in the future years. The study will help the scientific management of Sci-Tech Novelty search work for Novelty search organizations.展开更多
基金This work has been supported by the National 863 Key Project Grant No. 2008AA042901, National Natural Science Foundation of China Grant No.70631003 and No.90718037, Foundation of Hefei University of Technology Grant No. 2010HGXJ0083.
文摘Electricity demand forecasting plays an important role in smart grid expansion planning.In this paper,we present a dynamic GM(1,1) model based on grey system theory and cubic spline function interpolation principle.Using piecewise polynomial interpolation thought,this model can dynamically predict the general trend of time series data.Combined with low-order polynomial,the cubic spline interpolation has smaller error,avoids the Runge phenomenon of high-order polynomial,and has better approximation effect.Meanwhile,prediction is implemented with the newest information according to the rolling and feedback mechanism and fluctuating error is controlled well to improve prediction accuracy in time-varying environment.Case study using the living electricity consumption data of Jiangsu province in 2008 is presented to demonstrate the effectiveness of the proposed model.
文摘Grey system theory has been widely applied to many domains such as economy, agriculture, management, Social Sciences and so on. Based on the theory of grey system, this paper established GM（1,1） grey predict model for the first time to forecast The number of Scitech novelty search item and The staff number of Sci-Tech Novelty Search. The predicting results are almost close to the actual values, which shows that the model is reliable so that the models could be used to forecast the two factors in the future years. The study will help the scientific management of Sci-Tech Novelty search work for Novelty search organizations.
