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Sustainability 2017, 9(7), 1181; doi:10.3390/su9071181

Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model

School of Economic and Management, China University of Petroleum (East China), Qingdao 266580, Shandong, China
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Received: 3 June 2017 / Revised: 30 June 2017 / Accepted: 1 July 2017 / Published: 6 July 2017
(This article belongs to the Section Energy Sustainability)
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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. View Full-Text
Keywords: energy demand; energy prediction; GM-ARIMA model; GM (1,1) model; ARIMA model; Shandong province energy demand; energy prediction; GM-ARIMA model; GM (1,1) model; ARIMA model; Shandong province
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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. (CC BY 4.0).

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Li, S.; Li, R. Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model. Sustainability 2017, 9, 1181.

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