Mathematical Problems in Engineering

Volume 2018, Article ID 3894723, 11 pages

https://doi.org/10.1155/2018/3894723

## Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

Correspondence should be addressed to Jianwei Mi; nc.ude.naidix@imwj

Received 29 August 2017; Accepted 13 February 2018; Published 25 March 2018

Academic Editor: Emilio Turco

Copyright © 2018 Jianwei Mi et al. This 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.

#### Abstract

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 firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first 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. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This 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 flash 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 production cost and improving the economic benefit, 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 field 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 artificial intelligence class, such as expert systems and artificial neural networks. Because there are many factors affecting the short-term power load and different prediction methods have different applications, none of these methods is applicable to all power systems, which need to choose different prediction models according to different 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 with less data and poor information. Its essence is to estimate the development law of an object containing incomplete information based on the principle of grey system analysis [20, 21]. Compared with other prediction methods, the grey prediction model has the characteristics of less data, high prediction precision, and no prior information. Therefore, it is suitable for short-term power load forecasting. China’s power load has both the certainty increased year by year and the uncertainty affected by external factors, which agrees with the characteristics of “small sample, poor information” of the grey system, so it is rational to use the grey model for modeling prediction [22–24]. However, the which is commonly used in the traditional grey prediction model is a biased exponential model. In particular, when the data fluctuates, its prediction error is too large to meet the requirements of the actual power load forecasting.

The traditional model is only used for the modeling and prediction of single time series to reveal the inherent development law of the single variable. But the actual power system often contains multiple factor variables coupled with each other; that is, each factor variable in its development process is affected by other factors and also affects other factors at the same time. In order to get the predicted value that agrees with the actual situation, we should take the comprehensive influences of various factors on the predicted variables into consideration.

The traditional grey prediction model has many problems to be solved, such as its complex improved methods, the fact that it cannot comprehensively consider the effects of influencing factors, its limited application scope, and its prediction error failing to meet the requirement. Aimed at these problems, many scholars have proposed various improved methods [25, 26]. Based on the analysis of these improved methods, this paper firstly employs the main influencing factor from various influencing factors using the grey correlation analysis. And then it establishes an improved exponential smoothing grey prediction model combining the exponential smoothing method and the characteristics of short-term power load, which carries out short-term load forecasting using the historical data of power load and influencing factors. The simulated results show that the method has a satisfactory prediction effect on the short-term power load. The validity and feasibility of the prediction model are of great significance to solve the problem of the short-term power load forecasting in the development of smart grids in the future.

#### 2. The Exponential Smoothing Method and Traditional Grey Prediction Model

##### 2.1. The Exponential Smoothing Method

The exponential smoothing method is also a straightforward time series prediction method, which has the characteristics of simple calculation and convenient use. It is often applied to short-term and ultrashort-term power load forecasting and has high precision [27]. The prediction for the linear model of the exponential smoothing method is shown in where is the current period, is the predicted period in advance, and is the predicted value in period. The parameters and are determined by where is the smooth coefficient and is the original value at time . and are the first smoothing values at time and time , respectively. and are the second smoothing values at time and time , respectively, as well as , where and represent the first smoothing value and the second smoothing value at the initial time, respectively, and represents the original value at the initial time.

From (2), we can know that the smooth coefficient value directly affects the accuracy of the predicted value. Therefore, the most critical step in the exponential smoothing method is to determine the smooth coefficient. And it can help reduce the prediction error by finding out the optimal value. The methods commonly used to determine the smooth coefficient are the empirical estimation method, trial and error, and others. However, the common drawback of the two methods is that forecasting researchers must perform the iterations and calculations several times to obtain an optimal value which has a tight relationship with the knowledge, professional experience, and the number of calculations of the forecasting researchers. What is more, the forecasting process of this method (which is used to determine the smooth coefficient by the empirical estimation and trial-and-error methods) needs human intervention and thus has low automation and is an inefficient solving method. To overcome the drawback of the above two methods, the 0.618 method [28] can be used to search for the optimal smooth coefficient. However, the optimum result of the 0.618 method depends mainly on the objective function chosen.

##### 2.2. The Traditional Grey Prediction Model

The grey prediction model is one of the core contents of the grey system theory. The most commonly used grey prediction model in power load forecasting is the model, whose parameters indicate that the model establishes a first-order differential equation for one predicted variable to make predictions. As shown in Figure 1, the traditional grey prediction modeling process mainly includes accumulated generation, grey parameters calculation, solving the differential equation, and inverse accumulated generation. The detailed procedures can be found in [29]. The advantage of the traditional grey prediction model is that there is not much demand for the sample and it can get a better prediction effect in the case of few data samples. The disadvantage is that it can only make predictions for a single variable and requires that the data change be gentle and in accordance with the exponential change law; thus, the prediction effect is not satisfactory in case of data fluctuation.