Forecasting the Energy Consumption of China by the Grey Prediction Model

Abstract

Energy consumption has important significance for every country in the world. To successfully predict the future energy consumption by a mathematical method is very important for relevant scientific study. In this article, China, the rising power of the world and one of the biggest energy consumption countries, is significantly taken as the research target for grey theory prediction. According to the actual energy consumption statistics of China from 1998 to 2006, this article established the grey model GM(1,1) for the total energy, coal energy and clean energy consumption of China, respectively, with a corresponding precision check-up thereafter. The results show the three prediction models are all in the first precision level, which is suitable for simulating and forecasting the original data sequences of the energy consumption characterized by grey type. By comparing the results of the three models, all three energy consumption types are in the trending upward, but clean energy consumption is especially getting the upper hand.

Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting

“…Beyond this, statistical methods usually require that the data conform to statistical assumptions, such as having a normal distribution [17], yet energy consumption data often do not conform to these usual statistical assumptions [17], limiting the forecasting capabilities of statistical methods. Therefore, to construct an energy demand prediction model, a forecasting method is needed that works well with small samples and without making any statistical assumptions [18]. One of the grey prediction models, GM(1,1), has drawn our attention to energy demand forecasting [2].…”

A Novel Grey Prediction Model Combining Markov Chain with Functional-Link Net and Its Application to Foreign Tourist Forecasting

“…However, prediction performance of econometric methods can be significantly influenced by incomplete information associated with explanatory factors; and models for time series, such as ARIMA [1] and Box-Jenkins models, usually require large size of samples to obtain reasonable prediction accuracy [2][3][4][5]. Neural networks, such as multilayer perceptron and support vector regression, have also been applied to demand forecasting [6,7].…”

“…For time series prediction, GM(1,1) is among the most frequently used grey prediction models [10]. It requires only four recent samples to derive reliable and acceptable prediction accuracy [5], and has been widely applied to various decision problems involving management, economics, and engineering [2][3][4][11][12][13][14][15][16]. To better improve the prediction performance of the original GM(1,1) model, several versions combining with computational intelligence have been proposed, such as models with self-adaptive intelligence [17], neural-network-based grey prediction for electricity consumption prediction [18,19], PGM(1,1) using particle swarm optimization to determine the development coefficient [20], GM(1,1) models with online sequential extreme learning machine [21], an optimized nonlinear grey Bernoulli model [22], an adaptive GM(1,1) for electricity consumption [3], and grey wave forecasting through qualified contour sequences [23].…”

Predicting Foreign Tourists for the Tourism Industry Using Soft Computing-Based Grey–Markov Models

“…Grey prediction models [2] have drawn much attention because they can characterize an unknown system from limited data [3][4][5], without requiring conformance to statistical assumptions, such as normal distributions. The widely used grey model with a first order differential equation and one variable, GM(1,1), for example, can be set up using only four recent sample data points [6][7][8][9][10][11][12][13].…”