A Grey Prediction Approach to Forecasting Energy Demand in China

Abstract

Energy demand forecasting plays an important role in decision making. A mathematical model known as grey model GM(1,1) has been, herewith, employed successfully in the estimation of energy demand. In order to improve the forecast accuracy, the original GM(1,1) models are improved by using three methodologies of the 3-points average technology and the residual modification. This method takes into account the general trend series and random fluctuations about this trend. Two experiments were carried out respectively on the electricity demand and energy production from 1984 to 2006 in China to demonstrate the effectiveness of our approach. Furthermore, this improved grey forecasting model was used to forecast China's electricity demand and energy production, which shows that the modified forecasting model is more reliable and has a higher forecast accuracy than the GM (1,1). The forecasted results indicate that China's final energy demand will increase rapidly in the period 2007-2015. The results provide scientific basis for the planned development of energy supply in China.

Using the fractional order method to generalize strengthening buffer operator and weakening buffer operator

“…Case 6: The energy production forecasting in China [27] The 1985-1989 data are used for model building, while the 1990-1995 data are used as an ex-post testing data set. The results given by the GM(1,1) model and 1.5WGM(1,1) as well as the observed values are shown in Table VII.…”

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

“…This leads to inevitable environmental impacts on China. Undoubtedly, energy demand prediction has become increasingly important when devising sustainable development plans for China [16]. The proposed N-FLNGM(1,1) has demonstrated its potential for energy demand forecasting.…”

“…A large number of samples are required for multivariate regression and time series analysis like the autoregressive integrated moving average (ARIMA) [14]. The performance of the above-mentioned methods can be significantly affected by the number and the representativeness of observations [15,16]. However, using long-term data to build energy consumption prediction models may be impractical Sustainability 2017, 9, 1166 2 of 14 because the average annual growth rate of energy consumption is high and unstable.…”

“…One of the grey prediction models, GM(1,1), has drawn our attention to energy demand forecasting [2]. Indeed, how factors such as income and population influence the demand for energy is not clear, so energy demand forecasting can be regarded as a grey system problem [2,16].…”

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].…”