Power Investment Prediction Based on the Improved GM(1,1) Model

Shuang-qing LIN, Rui WANG, Ming-wei LI, Jian-jia ZHOU, Yan DENG, Tao LUO, Ji-yang ZHANG

Abstract


Power investment prediction plays an important part in fund management of power company. The traditional GM(1,1) model has been widely used in prediction. However, as the background value of traditional model has large error, using this model to predict power investment is unsuitable. In this paper, an improved GM(1,1) model is proposed. The improved model has better prediction accuracy in power investment prediction by using cubic spline interpolation to optimize background value. Case study using the power investment data of 6 cities from 2005 to 2010 demonstrates the feasibility and validity of the improved model.

Keywords


Power investment prediction, GM(1,1) Model, Improved GM(1,1) model, Optimization of background value


DOI
10.12783/dtetr/ecar2018/26412

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