Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model
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...
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| Series: | Sustainability |
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| Online Access: | https://www.mdpi.com/2071-1050/9/7/1181 |
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doaj-a9cb591cb5924b7191b2b3488db4b4f92020-11-24T23:07:05ZengMDPI AGSustainability2071-10502017-07-0197118110.3390/su9071181su9071181Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM ModelShuyu Li0Rongrong Li1School of Economic and Management, China University of Petroleum (East China), Qingdao 266580, Shandong, ChinaSchool of Economic and Management, China University of Petroleum (East China), Qingdao 266580, Shandong, ChinaTo 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.https://www.mdpi.com/2071-1050/9/7/1181energy demandenergy predictionGM-ARIMA modelGM (1,1) modelARIMA modelShandong province |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Shuyu Li Rongrong Li |
| spellingShingle |
Shuyu Li Rongrong Li Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model Sustainability energy demand energy prediction GM-ARIMA model GM (1,1) model ARIMA model Shandong province |
| author_facet |
Shuyu Li Rongrong Li |
| author_sort |
Shuyu Li |
| title |
Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model |
| title_short |
Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model |
| title_full |
Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model |
| title_fullStr |
Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model |
| title_full_unstemmed |
Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model |
| title_sort |
comparison of forecasting energy consumption in shandong, china using the arima model, gm model, and arima-gm model |
| publisher |
MDPI AG |
| series |
Sustainability |
| issn |
2071-1050 |
| publishDate |
2017-07-01 |
| description |
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. |
| topic |
energy demand energy prediction GM-ARIMA model GM (1,1) model ARIMA model Shandong province |
| url |
https://www.mdpi.com/2071-1050/9/7/1181 |
| work_keys_str_mv |
AT shuyuli comparisonofforecastingenergyconsumptioninshandongchinausingthearimamodelgmmodelandarimagmmodel AT rongrongli comparisonofforecastingenergyconsumptioninshandongchinausingthearimamodelgmmodelandarimagmmodel |
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1725620209891409920 |