Abstract
The construction of smart grid will comprehensively enhance the intelligent level of every step in the power grid of our country. The data prediction ability determines the quality of smart grid. This paper addresses situations in which the prediction accuracy of the Grey Model (GM (1, 1) model) is high for non-negative smooth monotonic sequences but inadequately low for non-stationary sequences, and isolates the trending sequence from the non-stationary time series using a numerical filtering algorithm, which is then used to make predictions. Numerical examples demonstrate that this method can improve the prediction accuracy of the GM (1, 1) model.
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Acknowledgements
This research was partially supported by National Natural Science Foundation of China, grant No.71101041, National 863 Project, grant No. 2011AA05A116, Foundation of Higher School Outstanding Talents Grant No. 2012SQRL009 and National Innovative Experiment Program No.111035954.
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Wang, X. (2014). Electricity Consumption Prediction Based on Non-stationary Time Series GM (1, 1) Model and Its Application in Power Engineering. In: Wang, W. (eds) Mechatronics and Automatic Control Systems. Lecture Notes in Electrical Engineering, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-319-01273-5_105
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DOI: https://doi.org/10.1007/978-3-319-01273-5_105
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