CN105956716A - Total social electricity consumption prediction method based on industry economy and electricity relationship - Google Patents
Total social electricity consumption prediction method based on industry economy and electricity relationship Download PDFInfo
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Abstract
本发明提供一种基于行业经济与用电关系的全社会用电量预测方法,获取被研究区域内的固定资产投资、黑色金属、化工、非金属、有色金属及装备制造业、商品房销售的经济数据;相关产业及生活用电量数据;以及平均气温数据;然后建立经济预测模型进行社会用电量的预测。本发明为年度及以内层面的全社会用电量预测提供了一种科学准确的预测手段,尤为适用于当前经济新常态和极端天气频发的大形势。The present invention provides a method for forecasting electricity consumption of the whole society based on the relationship between industry economy and electricity consumption, and obtains the economic data of fixed asset investment, ferrous metal, chemical industry, non-metal, nonferrous metal and equipment manufacturing, and commercial housing sales in the research area. data; relevant industry and domestic electricity consumption data; and average temperature data; and then establish an economic forecasting model to predict social electricity consumption. The present invention provides a scientific and accurate forecasting method for forecasting the electricity consumption of the whole society at the annual and sub-levels, and is especially suitable for the current economic new normal and frequent extreme weather situations.
Description
技术领域technical field
本发明涉及电力市场需求预测技术领域,具体是一种基于行业经济与用电关系的全社会用电量预测方法。The invention relates to the technical field of power market demand prediction, in particular to a method for predicting electricity consumption of the whole society based on the relationship between industry economy and electricity consumption.
背景技术Background technique
电力市场需求预测是能源主管部门和电网企业的一项重要基础性工作,准确的用电量预测结果可有效支撑能源主管部门科学编制发电计划和电力发展规划,以及支撑电网企业准确制定生产计划与经营决策。Electricity market demand forecasting is an important basic work for energy authorities and power grid enterprises. Accurate power consumption forecast results can effectively support energy authorities to scientifically formulate power generation plans and power development plans, and support power grid enterprises to accurately formulate production plans and Business decisions.
然而当前经济新常态下产业结构和工业内部结构调整升级明显加快,工业内部各行业也在不断加大产品结构转型升级和节能降耗力度,导致各行业用电量与其增加值间的耦合关系大大减弱,加之近年来极端天气频发,经济与用电间传统的稳定关系已发生显著变化。诸如GDP回归、产值单耗、弹性系数等基于经济总量的传统预测手段已经失灵,甚至从固定资产投资-分产业增加值-分产业用电量的经济电力传导预测方法预测结果亦持续偏高,用电需求准确预测难度大大增加。However, under the current economic new normal, the adjustment and upgrading of the industrial structure and the internal structure of the industry have been significantly accelerated, and various industries within the industry are also continuously intensifying the transformation and upgrading of product structure and energy conservation and consumption reduction, resulting in a significant coupling relationship between electricity consumption and its added value in various industries. weakening, coupled with the frequent occurrence of extreme weather in recent years, the traditional stable relationship between the economy and electricity consumption has undergone significant changes. Traditional forecasting methods based on economic aggregates, such as GDP regression, unit consumption of output value, and elastic coefficient, have failed, and even the prediction results from the economic power transmission forecasting method of fixed asset investment-industry added value-industry electricity consumption continue to be high , the difficulty of accurately forecasting electricity demand has greatly increased.
相关学者对电力市场需求预测研究主要集中于电力负荷预测,而对于用电量预测大多仍停留在GDP回归、产值单耗、弹性系数等传统预测手段,部分学者构建的分产业经济电力传导预测方法,由于关注点仍停留在各产业角度,已不适用于产业结构调整明显加速、各行业经济与用电关系已发生明显变化的当前大形势。Relevant scholars' research on power market demand forecasting mainly focuses on power load forecasting, while most of the power consumption forecasting still stays in the traditional forecasting methods such as GDP regression, output value unit consumption, and elastic coefficient. , because the focus is still on the perspective of each industry, it is no longer applicable to the current situation where the adjustment of industrial structure has been significantly accelerated, and the relationship between the economy and electricity consumption of various industries has undergone significant changes.
发明内容Contents of the invention
本发明针对现有技术的不足,提供一种基于行业经济与用电关系的全社会用电量预测方法,为年度及以内层面的全社会用电量预测提供了一种科学准确的预测手段,尤为适用于当前经济新常态和极端天气频发的大形势。Aiming at the deficiencies of the prior art, the present invention provides a method for forecasting electricity consumption of the whole society based on the relationship between industry economy and electricity consumption, and provides a scientific and accurate forecasting means for forecasting electricity consumption of the whole society at the annual and sub-levels. It is especially suitable for the current economic new normal and the general situation of frequent extreme weather.
为解决上述技术问题,本发明采用如下技术方案:In order to solve the problems of the technologies described above, the present invention adopts the following technical solutions:
一种基于行业经济与用电关系的全社会用电量预测方法,包括如下步骤:A method for forecasting electricity consumption of the whole society based on the relationship between industry economy and electricity consumption includes the following steps:
(1)获取被研究区域的如下经济数据:总固定资产投资、房地产开发投资、装备制造业投资、烧碱产量、合成氨产量、钢材产量、水泥产量和十种有色金属产量季度累计数据,商品房销售面积、住宅销售面积和城镇化率年度数据,农村常住居民人均可支配收入、城镇常住居民人均可支配收入和居民消费价格指数单季数据,如下用电量数据:全社会、第二产业、工业、黑色金属冶炼及压延加工业、化学原料及化学制品制造业、非金属及矿物制品制造业、有色金属冶炼及压延加工业、装备制造业、第三产业和城乡居民生活季度用电量数据,如下气温数据:季度平均气温数据;(1) Obtain the following economic data of the research area: total fixed asset investment, real estate development investment, equipment manufacturing investment, caustic soda production, synthetic ammonia production, steel production, cement production and ten non-ferrous metal production quarterly cumulative data, commercial housing sales area , annual data on residential sales area and urbanization rate, single-season data on per capita disposable income of rural permanent residents, per capita disposable income of urban permanent residents and consumer price index, and data on electricity consumption as follows: whole society, secondary industry, industry, Ferrous metal smelting and rolling processing industry, chemical raw material and chemical product manufacturing industry, non-metal and mineral product manufacturing industry, non-ferrous metal smelting and rolling processing industry, equipment manufacturing industry, tertiary industry, and quarterly electricity consumption data of urban and rural residents are as follows Temperature data: quarterly average temperature data;
(2)构建以钢材季度累计产量作为被解释变量、季度累计房地产开发投资作为解释变量的逐季钢材产量计量经济预测模型,以黑色金属冶炼及压延加工业季度累计用电量作为被解释变量、钢材季度累计产量作为解释变量的逐季黑色金属冶炼及压延加工业用电量计量经济预测模型;(2) Construct an econometric forecasting model of quarterly steel production with the quarterly cumulative output of steel as the explained variable and the quarterly cumulative real estate development investment as the explanatory variable, taking the quarterly cumulative electricity consumption of the ferrous metal smelting and rolling processing industry as the explained variable, Quarterly accumulative production of steel products as an explanatory variable, an econometric forecasting model of electricity consumption in ferrous metal smelting and rolling processing industry quarter by quarter;
(3)构建以烧碱季度累计产量作为被解释变量、季度累计房地产开发投资作为解释变量的逐季烧碱产量计量经济预测模型,以合成氨季度累计产量作为被解释变量、季度累计总固定资产投资作为解释变量的逐季合成氨产量计量经济预测模型,以化学原料及化学制品制造业季度累计用电量作为被解释变量、烧碱和合成氨季度累计产量作为解释变量的逐季化学原料及化学制品制造业用电量计量经济预测模型;(3) Construct an econometric forecasting model of caustic soda production quarterly with the quarterly cumulative output of caustic soda as the explained variable and the quarterly cumulative real estate development investment as the explanatory variable, with the quarterly cumulative output of synthetic ammonia as the explained variable and the quarterly cumulative total investment in fixed assets as the explanation The econometric forecasting model of quarter-by-quarter synthetic ammonia production of variables, with the quarterly cumulative electricity consumption of chemical raw materials and chemical product manufacturing as the explained variable, and the quarterly cumulative production of caustic soda and synthetic ammonia as explanatory variables, the season-by-season electricity consumption of chemical raw materials and chemical product manufacturing Quantitative econometric forecasting models;
(4)构建以水泥季度累计产量作为被解释变量、季度累计房地产开发投资作为解释变量的逐季水泥产量计量经济预测模型,以非金属及矿物制品制造业季度累计用电量作为被解释变量、水泥季度累计产量作为解释变量的逐季非金属及矿物制品制造业用电量计量经济预测模型;(4) Construct a quarterly cement production econometric forecasting model with the quarterly cumulative output of cement as the explained variable and the quarterly cumulative real estate development investment as the explanatory variable, taking the quarterly cumulative electricity consumption of the non-metallic and mineral product manufacturing industry as the explained variable, Quarterly cumulative production of cement as an explanatory variable, an econometric forecasting model of electricity consumption in the non-metallic and mineral product manufacturing industry quarter by quarter;
(5)构建以十种有色金属季度累计产量作为被解释变量、季度累计房地产开发投资作为解释变量的逐季十种有色金属产量计量经济预测模型,以有色金属冶炼及压延加工业季度累计用电量作为被解释变量、十种有色金属季度累计产量作为解释变量的逐季有色金属冶炼及压延加工业用电量计量经济预测模型;(5) Construct an econometric forecasting model of ten non-ferrous metal production quarterly, with the quarterly cumulative output of ten non-ferrous metals as the explained variable and the quarterly cumulative real estate development investment as the explanatory variable, taking the quarterly cumulative electricity consumption of the non-ferrous metal smelting and rolling processing industry An econometric forecasting model of electricity consumption in the non-ferrous metal smelting and rolling processing industry quarter by quarter with quantity as the explained variable and quarterly accumulative output of ten non-ferrous metals as the explanatory variable;
(6)构建以装备制造业季度累计用电量作为被解释变量、季度累计装备制造业投资作为解释变量的逐季装备制造业用电量计量经济预测模型;(6) Construct a season-by-quarter econometric forecasting model of electricity consumption in the equipment manufacturing industry with the quarterly accumulated electricity consumption as the explained variable and the quarterly accumulated investment in the equipment manufacturing industry as the explanatory variable;
(7)构建以工业累计用电量作为被解释变量、以黑色金属冶炼及压延加工业、化学原料及化学制品制造业、非金属及矿物制品制造业、有色金属冶炼及压延加工业和装备制造业季度累计用电量之和为解释变量的逐季工业用电量计量经济预测模型,以第二产业累计用电量为被解释变量、工业累计用电量为解释变量的逐季第二产业用电量计量经济预测模型;(7) Constructing industrial accumulative power consumption as the explained variable, ferrous metal smelting and rolling processing industry, chemical raw material and chemical product manufacturing industry, non-metallic and mineral product manufacturing industry, non-ferrous metal smelting and rolling processing industry and equipment manufacturing The quarter-by-quarter industrial electricity consumption econometric forecasting model takes the sum of industrial quarterly cumulative electricity consumption as the explanatory variable, and the quarter-by-quarter secondary industry electricity consumption as the explained variable and industrial cumulative electricity consumption Electricity consumption econometric forecasting model;
(8)基于年度商品房销售面积减住宅销售面积,得到年度商业销售面积,基于前10年的商业销售面积计算得到本年商业使用面积,本年各季度商业使用面积均按本年值考虑,再基于历年各季平均气温,构建以第三产业用电量为被解释变量、商业使用面积和平均气温为解释变量的逐年同季第三产业用电量计量经济预测模型;(8) Based on the annual commercial housing sales area minus the residential sales area, the annual commercial sales area is obtained, and the commercial use area of the year is calculated based on the commercial sales area of the previous 10 years. The commercial use area in each quarter of the year is considered according to the current year value, and then Based on the average temperature in each season over the years, construct an econometric forecasting model of electricity consumption in the tertiary industry in the same quarter year by year with the electricity consumption of the tertiary industry as the explained variable, the area of commercial use and the average temperature as the explanatory variables;
(9)基于季度农村居民人均可支配收入、城镇居民人均可支配收入和年度城镇化率,计算得到季度城乡居民人均可支配收入,再基于历年各季平均气温,并考虑居民阶梯电价政策变量,构建以城乡居民生活用电量为被解释变量、城乡居民人均可支配收入、平均气温和居民阶梯电价政策为解释变量的逐年同季城乡居民生活用电量计量经济预测模型;(9) Based on the quarterly per capita disposable income of rural residents, the per capita disposable income of urban residents and the annual urbanization rate, the quarterly per capita disposable income of urban and rural residents is calculated, and then based on the average temperature of each season over the years, and considering the policy variables of the tiered electricity price for residents, Construct an econometric forecasting model of urban and rural residents' domestic electricity consumption year by year and the same quarter, with urban and rural residents' domestic electricity consumption as the explained variable, urban and rural residents' per capita disposable income, average temperature and residents' stepped electricity price policy as explanatory variables;
(10)构建以季度累计全社会用电量为被解释变量,第二产业、第三产业和城乡居民生活季度累计用电量之和为解释变量的全社会用电量计量经济预测模型;(10) Construct an econometric prediction model of electricity consumption in the whole society with the quarterly accumulated electricity consumption of the whole society as the explained variable, and the sum of the quarterly accumulated electricity consumption of the secondary industry, the tertiary industry and urban and rural residents as the explanatory variable;
(11)根据被研究区域的后期经济社会发展走势,设定预测期房地产开发投资和装备制造业投资季度累计增速、农村居民人均可支配收入和城镇居民人均可支配收入当季增速、当季居民消费价格指数及年度城镇化率,预测期平均气温按常年考虑(取历年同季平均气温的平均值),带入(2)-(10)中构建的模型群,得到预测期被研究区域的全社会用电量预测结果。(11) According to the late economic and social development trend of the studied area, set the quarterly cumulative growth rate of real estate development investment and equipment manufacturing investment, the quarterly growth rate of rural residents’ per capita disposable income and urban residents’ per capita disposable income, and the current The quarterly consumer price index and annual urbanization rate, and the average temperature during the forecast period are taken into account in normal years (taking the average temperature of the same season over the years), and brought into the model group constructed in (2)-(10), and the forecast period is studied The predicted results of electricity consumption of the whole society in the region.
步骤(2)中,逐季钢材产量计量经济预测模型和黑色金属冶炼及压延加工业用电量计量经济预测模型分别如下:In step (2), the econometric forecasting model of quarterly steel production and the econometric forecasting model of electricity consumption in the ferrous metal smelting and rolling processing industry are as follows:
其中,t表示历史期t年,i表示历史期t年的第i季度(取值1、2、3、4),GCCLt,i、FDCTZt,i、CPIt,i和HSYDt,i分别表示历史期t年第i季度钢材累计产量、累计房地产开发投资、累计居民消费价格指数(以历史期起始年各季度=1,以定基形式计算得到其他年份各季度CPI,季度累计采用季度平均的方式)、黑色金属冶炼及压延加工业累计用电量,和均为待确定系数(基于最小二乘估计法得到),为反映被解释变量自身历史变化规律,在模型中可适当增加自回归和移动平均(AR和MA)项,模型除列出的线性形式外,还可以是对数(变量取对数log)形式。Among them, t represents the historical period of year t, i represents the i-th quarter of the historical period of year t (values 1, 2, 3, 4), GCCL t,i , FDCTZ t,i , CPI t,i and HSYD t,i Respectively represent the cumulative production of steel products in the i-th quarter of year t in the historical period, the cumulative real estate development investment, and the cumulative consumer price index (with each quarter of the starting year of the historical period = 1, the CPI of each quarter in other years is calculated in the form of a fixed basis, and the quarterly cumulative adopts quarterly average way), ferrous metal smelting and rolling processing industry cumulative electricity consumption, and All are undetermined coefficients (obtained based on the least squares estimation method). In order to reflect the historical variation of the explained variable itself, autoregressive and moving average (AR and MA) items can be appropriately added to the model. The model is not listed in the linear form In addition, it can also be in the form of logarithm (the variable takes the logarithm log).
步骤(3)中,逐季烧碱产量计量经济预测模型、合成氨产量计量经济预测模型和化学原料及化学制品制造业用电量计量经济预测模型分别如下:In step (3), the season-by-season caustic soda production econometric forecasting model, the synthetic ammonia production econometric forecasting model, and the chemical raw material and chemical product manufacturing industry electricity consumption econometric forecasting model are as follows:
其中,SJCLt,i、HCACLt,i和HGYDt,i分别表示历史期t年第i季度烧碱累计产量、合成氨累计产量和化学原料及化学制品制造业累计用电量,ZTZt-1,i表示历史期t-1年第i季度累计总固定资产投资,和均为待确定系数(基于最小二乘估计法得到),为反映被解释变量自身历史变化规律,在模型中可适当增加自回归和移动平均(AR和MA)项,模型除列出的线性形式外,还可以是对数(变量取对数log)形式。Among them, SJCL t,i , HCACL t,i and HGYD t,i respectively represent the cumulative production of caustic soda, synthetic ammonia and the cumulative electricity consumption of chemical raw materials and chemical products manufacturing in the i quarter of year t in the historical period, ZTZ t-1, i represents the cumulative total fixed asset investment in the i quarter of the historical period t-1, and All are undetermined coefficients (obtained based on the least squares estimation method). In order to reflect the historical variation of the explained variable itself, autoregressive and moving average (AR and MA) items can be appropriately added to the model. The model is not listed in the linear form In addition, it can also be in the form of logarithm (the variable takes the logarithm log).
步骤(4)中,逐季水泥产量计量经济预测模型和非金属及矿物制品制造业用电量计量经济预测模型分别如下:In step (4), the season-by-season cement production econometric forecasting model and the non-metal and mineral product manufacturing electricity consumption econometric forecasting model are as follows:
其中,SNCLt,i和FJSYDt,i分别表示历史期t年第i季度水泥累计产量和非金属及矿物制品制造业累计用电量,和均为待确定系数(基于最小二乘估计法得到),为反映被解释变量自身历史变化规律,在模型中可适当增加自回归和移动平均(AR和MA)项,模型除列出的线性形式外,还可以是对数(变量取对数log)形式。Among them, SNCL t,i and FJSYD t,i represent the cumulative output of cement and the cumulative electricity consumption of non-metallic and mineral products manufacturing in the i-quarter of the historical period t, respectively, and All are undetermined coefficients (obtained based on the least squares estimation method). In order to reflect the historical variation of the explained variable itself, autoregressive and moving average (AR and MA) items can be appropriately added to the model. The model is not listed in the linear form In addition, it can also be in the form of logarithm (the variable takes the logarithm log).
步骤(5)中,逐季十种有色金属产量计量经济预测模型和有色金属冶炼及压延加工业用电量计量经济预测模型分别如下:In step (5), the econometric forecasting models for the production of ten kinds of non-ferrous metals and the econometric forecasting models for the electricity consumption of the non-ferrous metal smelting and rolling processing industry are as follows:
其中,YSCLt,i和YSYDt,i分别表示历史期t年第i季度十种有色金属累计产量和有色金属冶炼及压延加工业累计用电量,和均为待确定系数(基于最小二乘估计法得到),为反映被解释变量自身历史变化规律,在模型中可适当增加自回归和移动平均(AR和MA)项,模型除列出的线性形式外,还可以是对数(变量取对数log)形式。Among them, YSCL t,i and YSYD t,i represent the cumulative output of ten nonferrous metals and the cumulative electricity consumption of nonferrous metal smelting and rolling processing in the i quarter of year t, respectively. and All are undetermined coefficients (obtained based on the least squares estimation method). In order to reflect the historical variation of the explained variable itself, autoregressive and moving average (AR and MA) items can be appropriately added to the model. The model is not listed in the linear form In addition, it can also be in the form of logarithm (the variable takes the logarithm log).
步骤(6)中,逐季装备制造业用电量计量经济预测模型如下:In step (6), the quarterly econometric forecasting model of electricity consumption in the equipment manufacturing industry is as follows:
其中,ZBYDt,i和ZBTZt,i分别表示历史期t年第i季度装备制造业累计用电量和装备制造业累计投资,和均为待确定系数(基于最小二乘估计法得到),为反映被解释变量自身历史变化规律,在模型中可适当增加自回归和移动平均(AR和MA)项,模型除列出的线性形式外,还可以是对数(变量取对数log)形式。Among them, ZBYD t,i and ZBTZ t,i represent the accumulative power consumption of equipment manufacturing industry and the accumulative investment of equipment manufacturing industry in quarter i of year t in the historical period, respectively. and All are undetermined coefficients (obtained based on the least squares estimation method). In order to reflect the historical variation of the explained variable itself, autoregressive and moving average (AR and MA) items can be appropriately added to the model. The model is not listed in the linear form In addition, it can also be in the form of logarithm (the variable takes the logarithm log).
步骤(7)中,逐季工业用电量计量经济预测模型和第二产业用电量计量经济预测模型分别如下:In step (7), the quarterly industrial electricity consumption econometric forecasting model and the secondary industry electricity consumption econometric forecasting model are as follows:
其中,GYYDt,i和ERCYDt,i分别表示历史期t年第i季度工业累计用电量和第二产业累计用电量,和均为待确定系数(基于最小二乘估计法得到),为反映被解释变量自身历史变化规律,在模型中可适当增加自回归和移动平均(AR和MA)项。Among them, GYYD t,i and ERCYD t,i represent the cumulative electricity consumption of industry and the cumulative electricity consumption of the secondary industry in the i quarter of the historical period t, respectively, and Both are undetermined coefficients (obtained based on the least squares estimation method). In order to reflect the historical variation of the explained variable itself, autoregressive and moving average (AR and MA) items can be appropriately added to the model.
步骤(8)中,年度商业使用面积计算公式和逐年同季第三产业用电量计量经济预测模型分别如下:In step (8), the calculation formula of annual commercial use area and the econometric forecasting model of electricity consumption of the tertiary industry in the same quarter year by year are as follows:
其中,SSMJt表示历史期t年商业使用面积,SFXMJj和ZXMJj分别表示历史期j年商品房销售面积和住宅销售面积,SCYDt,i和PJWDt,i分别表示历史期t年第i季度第三产业用电量和平均气温,和(i取1,2,3,4)均为待确定系数(基于最小二乘估计法得到),为反映第三产业用电量自身历史变化规律,在模型中可适当增加自回归和移动平均(AR和MA)项,模型除列出的线性形式外,还可以是对数(变量取对数log)形式。Among them, SSMJ t represents the commercial use area in the historical period t, SFXMJ j and ZXMJ j represent the commercial housing sales area and residential sales area in the historical period j, respectively, SCYD t,i and PJWD t,i represent the i quarter of the historical period t, respectively Tertiary industry electricity consumption and average temperature, and (i takes 1, 2, 3, 4) are undetermined coefficients (obtained based on the least squares estimation method), in order to reflect the historical change law of the electricity consumption of the tertiary industry itself, autoregressive and moving average can be appropriately added to the model (AR and MA), the model can also be in logarithmic (logarithmic log) form in addition to the listed linear form.
步骤(9)中,季度城乡居民人均可支配收入计算公式和逐年同季城乡居民生活用电量计量经济预测模型分别如下:In step (9), the calculation formula of quarterly per capita disposable income of urban and rural residents and the econometric forecasting model of domestic electricity consumption of urban and rural residents in the same quarter year by year are as follows:
其中,JMSRt,i、CZSRt,i、NCSRt,i和JMYDt,i分别表示历史期t年第i季度城乡居民人均可支配收入、城镇常住居民人均可支配收入、农村城镇居民人均可支配收入和城乡居民生活用电量,CZHLt,4表示历史期t年第4季度(按年底值考虑)城镇化率,CPI't,i表示历史期t年第i季度当季定基居民消费价格指数,JTDJt,i表示居民阶梯电价政策哑元变量(处于实施期,取1,否则取0),Tt,i表示历史期t年居民阶梯电价政策在第i季度实施的总季度数(实施前取0),|x,y|表示取x或取y,采用或的形式主要用于反映居民阶梯电价政策对城乡居民生活用电量存在抑制作用、且抑制性在不断减弱(基于数据统计规律),ki为根据模型最佳拟合效果而确定的参数,和均为待确定系数(基于最小二乘估计法得到),为反映被解释变量自身历史变化规律,在模型中可适当增加自回归和移动平均(AR和MA)项,模型除列出的线性形式外,还可以是对数(变量取对数log)形式。Among them, JMSR t,i , CZSR t,i , NCSR t,i and JMYD t,i represent the per capita disposable income of urban and rural residents, the per capita disposable income of urban permanent residents, and the per capita disposable income of rural urban residents in the i quarter of historical period t, respectively. Disposable income and electricity consumption of urban and rural residents, CZHL t,4 represents the urbanization rate in the fourth quarter of year t (considered at the end of the year) in the historical period, CPI' t,i represents the fixed-based residential consumption in the quarter i of year t in the historical period Price index, JTDJ t,i represents the dummy variable of the tiered electricity price policy for residents (it is in the implementation period, takes 1, otherwise takes 0), T t,i represents the total number of quarters in which the tiered electricity price policy for residents in the historical period t was implemented in the i quarter (take 0 before implementation), |x,y| means take x or take y, use or The form of is mainly used to reflect that the tiered electricity price policy for residents has an inhibitory effect on the electricity consumption of urban and rural residents, and the inhibitory effect is constantly weakening (based on the statistical law of data). ki is a parameter determined according to the best fitting effect of the model. and All are undetermined coefficients (obtained based on the least squares estimation method). In order to reflect the historical variation of the explained variable itself, autoregressive and moving average (AR and MA) items can be appropriately added to the model. The model is not listed in the linear form In addition, it can also be in the form of logarithm (the variable takes the logarithm log).
步骤(10)中,逐季全社会用电量计量经济预测模型如下:In step (10), the econometric forecasting model of electricity consumption of the whole society is as follows:
其中,QSHYDt,i、SCYD′t,i和JMYD′t,i分别表示历史期t年第i季度累计全社会用电量、第三产业用电量和城乡居民生活用电量,和均为待确定系数(基于最小二乘估计法得到)。Among them, QSHYD t,i , SCYD′ t,i and JMYD′ t,i represent the cumulative electricity consumption of the whole society, the electricity consumption of the tertiary industry and the electricity consumption of urban and rural residents in the i quarter of the historical period t, respectively. and Both are undetermined coefficients (obtained based on the least squares estimation method).
本发明提出了一种基于行业经济与用电关系的全社会用电量预测方法,该方法充分考虑了各行业经济与用电关系变化以及天气因素对第三产业和居民生活用电的影响,预测准确性大幅提高,彻底改变了当前传统预测手段预测结果持续偏高的被动局面,能够为能源主管部门和电力市场分析预测人员开展年度及以内全社会用电量预测工作提供科学准确的参考依据。该预测方法在经济新常态下经济总量与用电总量间耦合关系大大减弱、行业经济与用电关系(表现为行业增加值和行业用电量间关系)已发生变化、天气因素对第三产业和居民生活用电影响难以忽略等大形势下而提出,由于既考虑了各行业经济与用电关系变化,又考虑了天气因素对用电影响,并形成了科学的预测方法体系,预测准确率大幅提升,可作为年度及以内全社会用电量预测的一种有效方法。The present invention proposes a method for predicting electricity consumption of the whole society based on the relationship between industry economy and electricity consumption. The method fully considers the changes in the relationship between the economy and electricity consumption of various industries and the impact of weather factors on the tertiary industry and residents' daily electricity consumption. The prediction accuracy has been greatly improved, which has completely changed the current passive situation where the traditional forecasting results continue to be high, and can provide scientific and accurate references for energy authorities and power market analysts and forecasters to carry out annual and domestic electricity consumption forecasting work . Under the new economic normal, the coupling relationship between economic aggregate and total electricity consumption is greatly weakened, the relationship between industry economy and electricity consumption (expressed as the relationship between industry added value and industry electricity consumption) has changed, and weather factors affect the first It is proposed under the circumstances that the impact of the tertiary industry and residents’ electricity consumption is difficult to ignore. Since it not only considers the changes in the relationship between the economy and electricity consumption of various industries, but also considers the impact of weather factors on electricity consumption, and has formed a scientific forecasting method system. The accuracy rate has been greatly improved, and it can be used as an effective method for forecasting the electricity consumption of the whole society at and within the year.
具体实施方式detailed description
本发明基于各行业经济与用电关系、天气因素对用电量影响角度,提供了一种基于行业经济与用电关系的全社会用电量预测方法。Based on the relationship between the economy and electricity consumption of various industries and the influence of weather factors on electricity consumption, the present invention provides a method for forecasting electricity consumption of the whole society based on the relationship between industry economy and electricity consumption.
以下以安徽省2015年全社会用电量预测误差回测和2016年预测为例,进一步说明本发明的具体实施方式。The specific implementation of the present invention will be further described below by taking Anhui Province's 2015 whole-society electricity consumption forecast error backtest and 2016 forecast as examples.
步骤(1):在安徽省统计局网站中进度数据和统计年鉴中获取该方法所需的安徽省经济数据,在安徽省电力公司获取用电量数据,在安徽省气象局获取平均气温数据;Step (1): Obtain the Anhui economic data required by the method from the progress data and the statistical yearbook on the Anhui Provincial Bureau of Statistics website, obtain the electricity consumption data from the Anhui Provincial Electric Power Company, and obtain the average temperature data from the Anhui Provincial Meteorological Bureau;
步骤(2):构建逐季钢材累计产量和黑色金属冶炼及压延加工业累计用电量计量经济预测模型;Step (2): Construct an econometric forecasting model for cumulative steel production and ferrous metal smelting and rolling processing industry cumulative electricity consumption;
GCCLt,i=167.8914+0.7086(FDCTZt,i/CPIt,i) (R2=0.999)GCCL t,i =167.8914+0.7086(FDCTZ t,i /CPI t,i ) (R 2 =0.999)
log(HSYDt,i)=6.2063+1.0021log(GCCLt,i)+[AR(4)=-0.2296,MA(1)=-0.9758](R2=0.999)log(HSYD t,i )=6.2063+1.0021 log(GCCL t,i )+[AR(4)=-0.2296, MA(1)=-0.9758] (R 2 =0.999)
步骤(3):构建逐季烧碱累计产量、合成氨累计产量和化学原料及化学制品制造业累计用电量计量经济预测模型;Step (3): Build an econometric forecasting model for the cumulative production of caustic soda, the cumulative production of synthetic ammonia, and the cumulative electricity consumption of chemical raw materials and chemical products manufacturing industry;
log(SJCLt,i)=-2.4702+0.8219log(FDCTZt,i/CPIt,i)+[AR(1)=0.8377,MA(4)=0.9883] (R2=0.997)log(SJCL t,i )=-2.4702+0.8219log(FDCTZ t,i /CPI t,i )+[AR(1)=0.8377, MA(4)=0.9883] (R 2 =0.997)
log(HCACLt,i)=-780.6135+76.9629log(ZTZt-1,i/CPIt-1,i)+[AR(4)=0.9441,MA(4)=-0.9989] (R2=0.999)log(HCACL t,i )=-780.6135+76.9629log(ZTZ t-1,i /CPI t-1,i )+[AR(4)=0.9441,MA(4)=-0.9989] (R 2 =0.999 )
log(HGYDt,i)=8.4297+0.0853log(SJCLt,i)+0.9588log(HCACLt,i)+[AR(3)=0.8210,MA(1)=-1.0000] (R2=0.999)log(HGYD t,i )=8.4297+0.0853log(SJCL t,i )+0.9588log(HCACL t,i )+[AR(3)=0.8210, MA(1)=-1.0000] (R 2 =0.999)
步骤(4):构建逐季水泥累计产量和非金属及矿物制品制造业累计用电量计量经济预测模型;Step (4): Build an econometric forecasting model for the cumulative production of cement quarter by season and the cumulative electricity consumption of non-metallic and mineral product manufacturing industries;
SNCLt,i=114.9+2.9127(FDCTZt,i/CPIt,i)+[MA(4)=0.9617] (R2=0.999)SNCL t,i =114.9+2.9127(FDCTZ t,i /CPI t,i )+[MA(4)=0.9617] (R 2 =0.999)
log(FJSYDt,i)=13.4808+0.0001SNCLt,i-0.0578*Dt,i+[AR(4)=0.9805,MA(1)=0.9580] (R2=0.999)log(FJSYD t,i )=13.4808+0.0001SNCL t,i -0.0578*D t,i +[AR(4)=0.9805,MA(1)=0.9580] (R 2 =0.999)
其中,根据安徽省实际情况在非金属及矿物制品制造业累计用电量计量经济预测模型中增加了哑元变量Dt,i(2013年第1季度=1,其余季度=0),表示2013年国家水泥行业新政对非金属及矿物制品制造业用电量的影响。Among them, according to the actual situation of Anhui Province, the dummy variable D t,i (the first quarter of 2013 = 1, and the rest of the quarter = 0) was added to the econometric forecasting model of the cumulative electricity consumption of the non-metallic and mineral product manufacturing industry, indicating that in 2013 The impact of the new policy of the national cement industry on the electricity consumption of non-metallic and mineral product manufacturing industries.
步骤(5):构建逐季十种有色金属累计产量和有色金属冶炼及压延加工业累计用电量计量经济预测模型;Step (5): Build an econometric forecasting model for the cumulative production of ten non-ferrous metals and the cumulative electricity consumption of non-ferrous metal smelting and rolling processing industries;
YSCLt,i=0.0274308+12.978665(FDCTZt,i/CPIt,i)+[AR(4)=0.135339,MA(1)=-0.99000] (R2=0.999)YSCL t,i =0.0274308+12.978665(FDCTZ t,i /CPI t,i )+[AR(4)=0.135339,MA(1)=-0.99000] (R 2 =0.999)
log(YSYDt,i)=7.5789+1.0215log(YSCLt,i)+0.5411*Dt,i+[AR(3)=0.4706,MA(1)=0.9990] (R2=0.989)log(YSYD t,i )=7.5789+1.0215log(YSCL t,i )+0.5411*D t,i +[AR(3)=0.4706,MA(1)=0.9990] (R 2 =0.989)
其中,根据安徽省实际情况在有色金属冶炼及压延加工业累计用电量计量经济预测模型中增加了哑元变量Dt,i(2013年第1季度=1,其余季度=0),表示2013年1季度受铜陵有色“双闪”项目试车影响(产量低、用电却很高)。Among them, according to the actual situation in Anhui Province, a dummy variable D t,i (the first quarter of 2013 = 1, and the rest of the quarter = 0) was added to the econometric forecasting model of the cumulative electricity consumption of the non-ferrous metal smelting and rolling processing industry, indicating that in 2013 In the first quarter of this year, it was affected by the test run of Tongling Nonferrous Metals'"DoubleFlash" project (low output, but high electricity consumption).
步骤(6):构建逐季装备制造业累计用电量计量经济预测模型;Step (6): Construct an econometric forecasting model of accumulative power consumption in the equipment manufacturing industry quarter by quarter;
log(ZBYDt,i)=-11.8129+1.4298log(ZBTZt,i/CPIt,i)+[AR(4)=0.9929,MA(3)=-0.9999] (R2=0.999)log(ZBYD t,i )=-11.8129+1.4298log(ZBTZ t,i /CPI t,i )+[AR(4)=0.9929,MA(3)=-0.9999] (R 2 =0.999)
表1Table 1
步骤(7):构建逐季工业累计用电量和第二产业累计用电量计量经济预测模型;Step (7): Construct an econometric forecasting model of quarterly industrial cumulative electricity consumption and secondary industry cumulative electricity consumption;
GYYDt,i=-45468.8328+1.4423(HSYDt,i+HGYDt,i+FJSYDt,i+YSYDt,i+ZBYDt,i)+[AR(4)=0.9471] (R2=0.999)GYYD t,i =-45468.8328+1.4423(HSYD t,i +HGYD t,i +FJSYD t,i +YSYD t,i +ZBYD t,i )+[AR(4)=0.9471] (R 2 =0.999)
ERCYDt,i=5444.8156+1.022168GYYDt,i (R2=1.0)ERCYD t,i =5444.8156+1.022168GYYD t,i (R 2 =1.0)
步骤(8):构建综合考虑商业使用面积和平均气温的逐年同季第三产业用电量计量经济预测模型;Step (8): Constructing an econometric prediction model of electricity consumption of the tertiary industry in the same quarter year by year considering the commercial use area and the average temperature;
一季度:First quarter:
log(SCYDt,1)=4.22653+1.083092log(SSMJt)-0.0181455PJWDt,1+[MA(1)=0.99741] (R2=0.999)log(SCYD t,1 )=4.22653+1.083092log(SSMJ t )-0.0181455PJWD t,1 +[MA(1)=0.99741] (R 2 =0.999)
二季度:Second quarter:
log(SCYDt,2)=4.78395+0.97964log(SSMJt) (R2=0.997)log(SCYD t,2 )=4.78395+0.97964log(SSMJ t ) (R 2 =0.997)
三季度:third quater:
log(SCYDt,3)=3.62954+1.002962log(SSMJt)+0.044888PJWDt,3 (R2=0.995)log(SCYD t,3 )=3.62954+1.002962log(SSMJ t )+0.044888PJWD t,3 (R 2 =0.995)
四季度:Fourth quarter:
log(SCYDt,4)=5.27923+0.947806log(SSMJt)-0.0124086PJWDt,4 (R2=0.994)log(SCYD t,4 )=5.27923+0.947806log(SSMJ t )-0.0124086PJWD t,4 (R 2 =0.994)
步骤(9):构建综合考虑城乡居民人均可支配收入、平均气温和居民阶梯电价政策因素的逐年同季城乡居民生活用电量计量经济预测模型;Step (9): Construct an econometric forecasting model of urban and rural residents’ daily electricity consumption in the same quarter year by year, taking into account the per capita disposable income of urban and rural residents, the average temperature and residents’ ladder electricity price policy factors;
一季度:First quarter:
二季度:Second quarter:
三季度:third quater:
JMYDt,3=-2252059.2+308.65JMSRt,3+81324.98PJWDt,3-JTDJt,3(65385.07-7249.94(Tt,3)2.2) (R2=0.995)JMYD t,3 =-2252059.2+308.65JMSR t,3 +81324.98PJWD t,3 -JTDJ t,3 (65385.07-7249.94(T t,3 ) 2.2 ) (R 2 =0.995)
四季度:Fourth quarter:
JMYDt,4=51507.53+192.4007JMSRt,3-8852.032PJWDt,3-JTDJt,3(102553.83-138331.85(Tt,3)0.25)+[MA(1)=-0.997442] (R2=0.993)JMYD t,4 =51507.53+192.4007JMSR t,3 -8852.032PJWD t,3 -JTDJ t,3 (102553.83-138331.85(T t,3 ) 0.25 )+[MA(1)=-0.997442] (R 2 =0.993 )
步骤(10):构建以第二产业、第三产业和城乡居民生活季度累计用电量之和为解释变量的全社会用电量计量经济预测模型;Step (10): Construct an econometric prediction model of electricity consumption in the whole society with the sum of the cumulative electricity consumption of the secondary industry, the tertiary industry, and the living quarters of urban and rural residents as explanatory variables;
QSHYDt,i=-10604.68+1.011408(ERCYDt,i+SCYD't,i+JMYD't,i)QSHYD t,i =-10604.68+1.011408(ERCYD t,i +SCYD' t,i +JMYD' t,i )
表2Table 2
步骤(11):根据安徽省2016年经济社会发展走势,设定2016年各季度主要经济和气温指标取值,带入(2)-(10)中构建的模型群,预测2016年安徽省全社会用电量为1721.4亿千瓦时,同比增长4.98%,该预测结果较为贴切当前经济新常态下经济中高速增长、用电中低速增长的总体判断。Step (11): According to the economic and social development trend of Anhui Province in 2016, set the values of the main economic and temperature indicators in each quarter of 2016, bring them into the model group constructed in (2)-(10), and predict the overall growth rate of Anhui Province in 2016. Social electricity consumption was 172.14 billion kWh, a year-on-year increase of 4.98%. This prediction is more appropriate for the overall judgment of medium-to-high-speed economic growth and medium-to-low-speed growth in electricity consumption under the current economic new normal.
表3table 3
表4Table 4
以上所述实施方式仅仅是对本发明的优选实施方式进行描述,并非对本发明的范围进行限定,在不脱离本发明设计精神的前提下,本领域普通技术人员对本发明的技术方案作出的各种变形和改进,均应落入本发明的权利要求书确定的保护范围内。The above-mentioned embodiments are only descriptions of the preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, those skilled in the art may make various modifications to the technical solutions of the present invention. and improvements, all should fall within the scope of protection determined by the claims of the present invention.
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