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首页> 外文期刊>Sustainability >Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model
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Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model

机译：中国山东能源消耗的比较，中国山东山东模型，通用型号，ARIMA-GM模型

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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.
机译：科学预测山东省未来的能源需求，本研究选择了1995 - 2015年山东省的过去的能源需求作为研究对象。基于建筑模型数据序列，GM-ARIMA模型，GM（1,1）模型和ARIMA模型用于预测山东省2005 - 2015年数据的能源，结果是比较的结果实际结果。通过分析相对平均误差，我们发现GM-Arima模型具有更高的准确性，可以预测未来的能量需求数据。 GM-ARIMA模型的操作步骤如下：首先，预处理日期并确定GM（1,1）模型的尺寸。随后是建立新陈代谢GM（1,1）模型，并通过计算预测数据。然后，使用Arima残差错误来修改和测试模型。最后，分析了所获得的预测结果和误差。预测结果表明，山东省2016 - 2020年的能源需求将以平均年增长率为3.9％，而2020年，山东省能源需求将增加到2015年的约20％。

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