Abstract
To scientifically predict the future energy demand of Shandong province, this study chose the past energy demand of Shandong province during 1995–2015 as the research object. Based on building model data sequences, the GM-ARIMA model, the GM (1,1) model, and the ARIMA model were used to predict the energy demand of Shandong province for the 2005–2015 data, the results of which were then compared to the actual result. By analyzing the relative average error, we found that the GM-ARIMA model had a higher accuracy for predicting the future energy demand data. The operation steps of the GM-ARIMA model were as follows: first, preprocessing the date and determining the dimensions of the GM (1,1) model. This was followed by the establishment of the metabolism GM (1,1) model and by calculation of the forecast data. Then, the ARIMA residual error was used to amend and test the model. Finally, the obtained prediction results and errors were analyzed. The prediction results show that the energy demand of Shandong province in 2016–2020 will grow at an average annual rate of 3.9%, and in 2020, the Shandong province energy demand will have increased to about 20% of that in 2015.
Highlights
The energy consumption of Shandong province was 36,759.2 million tons of standard coal equivalent in 2015, and the average growth rate has been increasing by up to 5% over the past years [1,2,3]
Compared to the average relative error value of both autoregressive integrated moving average (ARIMA) and GM (1,1) models, and if the average relative error of composite model was small, we could take it for granted that the application of the GM-ARIMA model increased the prediction accuracy of energy demand compared to that of a single model, and it would arrive at a higher predictive value
The GM-ARIMA model is very effective for systems with large sample size that follow trends, and it can be appropriately applied for the future energy prediction of Shandong province
Summary
The energy consumption of Shandong province was 36,759.2 million tons of standard coal equivalent in 2015, and the average growth rate has been increasing by up to 5% over the past years [1,2,3]. Energy consumption inspired an economic boom, and an exact prediction of the future energy demand is required for accurate forecasting of the required energy supply. A significant number of studies have focused on energy demand prediction, especially during recent years [13,14,15,16]. Grey theory is a truly multidisciplinary and generic theory, originally developed by Deng [18]. The grey theory has been applied in many applications of systems analysis [24,25,26]
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