Research Article Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model Jianwei Mi , 1,2 Libin Fan, 1,2 Xuechao Duan , 1,2 and Yuanying Qiu 1,2 1 School of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, China 2 Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, No. 2 South Taibai Road, Xi’an 710071, China Correspondence should be addressed to Jianwei Mi; jwmi@xidian.edu.cn Received 29 August 2017; Accepted 13 February 2018; Published 25 March 2018 Academic Editor: Emilio Turco Copyright © 2018 Jianwei Mi et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It frstly determines the main factor afecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. Te improved prediction model frstly carries out the smoothing processing of the original power load data using the frst exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. Te frst exponential smoothing model uses the 0.618 method to search for the optimal smooth coefcient. Te prediction model can take the efects of the infuencing factors on the power load into consideration. Te simulated results show that the proposed prediction algorithm has a satisfactory prediction efect and meets the requirements of short-term power load forecasting. Tis research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval. 1. Introduction Short-term power load forecasting is a key issue for the operation and dispatch of power systems in order to prevent the serious consequences of fash and power failures. It is a prerequisite for the economic operation of power systems and the basis of dispatching and making startup-shutdown plans, which plays a key role in the automatic control of power systems [1–3]. Accurate power load forecasting not only helps users choose a more appropriate electricity consumption scheme and reduces a lot of electric cost expenditure while improving equipment utilization thus reducing the produc- tion cost and improving the economic beneft, but also is conducive to optimizing the resources of power systems, improving power supply capability and ultimately achieving the aim of energy conservation and emission reduction [4– 6]. As the power system is increasingly complicated and the degree of electricity marketization is further enhanced, how to quickly and accurately predict short-term power loads has become one of the popular topics in the feld of power load forecasting. As a fundamental research, power load forecasting has been investigated for a long time. Many experts and scholars have done a lot of research on prediction theory and methods and put forward several prediction models and methods [7– 11]. At present, the prediction method of power load can be divided into two categories [12–14]. One is the classical prediction method of statistical class, such as regression analysis, time series method, and grey prediction method. And the other is the novel prediction method of artifcial intelligence class, such as expert systems and artifcial neural networks. Because there are many factors afecting the short- term power load and diferent prediction methods have diferent applications, none of these methods is applicable to all power systems, which need to choose diferent prediction models according to diferent power load conditions [15–18]. Grey system theory was proposed in 1982 [19]. It is a novel algorithm of coping with the problem of uncertainty Hindawi Mathematical Problems in Engineering Volume 2018, Article ID 3894723, 11 pages https://doi.org/10.1155/2018/3894723