准确的长期能源需求预测对于能源规划和决策至关重要.然而,由于能源数据收集和统计方法不完善,许多区域的可用于能源需求预测的数据通常有限.本文在文献综述的基础上,提出了一种基于长期替代能源规划模型(LEAP)的混合建模方法,以提高这些区域能源需求预测的准确性.以中国湖南省为例,应用该混合模型对不同行业未来可能的能源需求和节能潜力进行了估算.基于Sankey能流图确定LEAP模型结构,采用Lesline矩阵和自回归移动平均(ARIMA)模型分别预测人口、产业结构和运输周转率.采用蒙特卡罗方法评估预测结果的不确定性.研究结果表明,混合模型结合情景分析对统计数据有限区域的长期能源需求提供了相对准确的预测,2030年基础情景下能源需求概率分布的平均标准差不超过0.15.预测结果可为识别节能潜力和制定能源发展路径提供参考依据.%An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.
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机译：准确的长期能源需求预测对于能源规划和决策至关重要.然而,由于能源数据收集和统计方法不完善,许多区域的可用于能源需求预测的数据通常有限.本文在文献综述的基础上,提出了一种基于长期替代能源规划模型(LEAP)的混合建模方法,以提高这些区域能源需求预测的准确性.以中国湖南省为例,应用该混合模型对不同行业未来可能的能源需求和节能潜力进行了估算.基于Sankey能流图确定LEAP模型结构,采用Lesline矩阵和自回归移动平均(ARIMA)模型分别预测人口、产业结构和运输周转率.采用蒙特卡罗方法评估预测结果的不确定性.研究结果表明,混合模型结合情景分析对统计数据有限区域的长期能源需求提供了相对准确的预测,2030年基础情景下能源需求概率分布的平均标准差不超过0.15.预测结果可为识别节能潜力和制定能源发展路径提供参考依据.%An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.
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