基于电力数据的经济景气指数模型研究与应用OA北大核心CSTPCD
Research and Application of Economic Prosperity-Index Model Based on Electricity Data
电力需求变化折射出经济运行的活跃度,电力数据具有准确性高、实时性强、可预测等优点.重点研究基于电力数据的经济景气指数(economic prosperity index based on power data,EPI-P)计算方法,以从电力视角把握经济变化周期.首先,通过时差相关系数和K-L信息量2 种指标从电力指标中筛选经济指标的先行、一致与滞后指标,再分别采用基于相关系数和基于用电量的指标权重划分方法,构建电力经济景气指数计算模型.在此基础上,基于某省的电力经济数据,开展电力经济景气指数实证研究.最后,基于季节性自回归移动平均(seasonal autoregressive integrated moving average,SARIMA)模型对该省未来电力需求数据进行预测,计算该省未来的经济景气指数,对该省未来的经济发展态势进行研判.结果表明,基于电力数据的经济景气指数计算模型能通过电力数据有效反映经济指标的变化趋势,且可用于未来经济发展态势研判.
Changes in electricity demand reflect the vibrancy of economic operations.Electricity data have advantages such as high accuracy,strong real-time capabilities,and predictability.This study calculates an economic-prosperity index based on electricity data to capture cyclical,economic changes from an electricity perspective.First,two indicators—the time-lagged correlation coefficient and the K-L information quantity—are used to identify leading,coincident,and lagging economic indicators.Indicator weight-division methods based on correlation coefficients and electricity consumption were used to construct a calculation model for the electricity data-based economic-prosperity index.We conducted empirical research on the electricity economic-prosperity index based on the economic-electricity data of a certain province.Finally,we predicted the future electricity-demand data of the province,calculated its future economic-prosperity index,and analyzed its future economic-development trend,based on the seasonal autoregressive integrated moving average(SARIMA)model.The results indicate that the economic prosperity-index calculation model based on electricity data can effectively reflect the trends of economic indicators through electricity data and can be used for future economic-development trend analysis.
张雪婷;鲁肖龙;吴浩;宫嘉炜;李映雪;王敏;戴奇奇;鞠立伟
国网江西省电力有限公司经济技术研究院,南昌市 330006华北电力大学经济与管理学院,北京市 102206
动力与电气工程
电力数据经济景气指数经济发展态势季节性自回归移动平均(SARIMA)模型
electricity dataeconomic prosperity indexeconomic development trendseasonal autoregressive integrated moving average(SARIMA)model
《电力建设》 2024 (007)
12-24 / 13
This work is supported by National Natural Science Foundation of China(No.72274060,No.71904049,No.72174062,No.72074074),Beijing Social Science Fund(No.23JCB039)and the Fundamental Research Funds for the Central Universities(No.2024FR006). 国家自然科学基金项目(72274060,71904049,72174062,72074074);北京市社会科学基金项目(23JCB039);中央高校基本科研业务费哲学社会科学繁荣计划专项项目(2024FR006)
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