CN105654197A - Grey theory-based comprehensive plan investment total prediction method - Google Patents
Grey theory-based comprehensive plan investment total prediction method Download PDFInfo
- Publication number
- CN105654197A CN105654197A CN201511017465.9A CN201511017465A CN105654197A CN 105654197 A CN105654197 A CN 105654197A CN 201511017465 A CN201511017465 A CN 201511017465A CN 105654197 A CN105654197 A CN 105654197A
- Authority
- CN
- China
- Prior art keywords
- factors
- investment
- total
- electricity
- comprehensive
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000005611 electricity Effects 0.000 claims abstract description 49
- 238000004364 calculation method Methods 0.000 claims abstract description 13
- 238000011161 development Methods 0.000 claims abstract description 10
- 238000004451 qualitative analysis Methods 0.000 claims abstract description 4
- 238000004445 quantitative analysis Methods 0.000 claims abstract description 4
- 238000013439 planning Methods 0.000 claims description 20
- 238000013277 forecasting method Methods 0.000 claims description 13
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 7
- 238000007726 management method Methods 0.000 claims description 5
- 238000010219 correlation analysis Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 abstract description 2
- 230000000875 corresponding effect Effects 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 3
- 238000009499 grossing Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000012417 linear regression Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000013213 extrapolation Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000010921 in-depth analysis Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
一种基于灰色理论的综合计划投入总盘预测方法，它包括如下步骤：a)选择关键影响因素：从经济发展类、公司经营类、投资绩效类三个方面分析影响综合计划投入总盘可能存在的影响因素；b)运用灰色预测方法来预测关键影响因素的变化趋势：借助工具，按照灰色预测的算法步骤，编写相应程序代码，运行程序之后便可得到地区GDP、全社会用电最高负荷、售电量、利润总额等关键影响因素未来的趋势测算值；c)确定关键影响因素的影响程度：借助层次分析法，综合定性分析和定量分析，将测算因素的定性排序转化为决策系数，再根据决策系数确定各因素变化对总盘投入的影响程度；d)预测综合计划投入规模：根据测算模型，得到综合计划投入年度预测结果。A method for predicting the total investment of comprehensive plans based on gray theory, which includes the following steps: a) Select key influencing factors: analyze the possible existence of factors affecting the total investment of comprehensive plans from three aspects: economic development, company operation, and investment performance. b) Using the gray prediction method to predict the changing trend of the key influencing factors: with the help of tools, according to the algorithm steps of the gray prediction, write the corresponding program code, and after running the program, you can get the regional GDP, the highest load of electricity consumption in the whole society, The estimated value of the future trend of key influencing factors such as electricity sales and total profit; c) Determining the degree of influence of key influencing factors: with the help of analytic hierarchy process, comprehensive qualitative analysis and quantitative analysis, the qualitative ranking of measurement factors is converted into decision coefficients, and then according to The decision-making coefficient determines the degree of influence of changes in various factors on the total investment; d) Forecasting the investment scale of the comprehensive plan: according to the calculation model, the annual forecast result of the comprehensive plan investment is obtained.
Description
技术领域technical field
本发明涉及的是一种基于灰色理论的综合计划投入总盘预测方法，属于企业综合计划管理中的投资决策技术领域。The invention relates to a gray theory-based method for predicting the total investment of comprehensive planning, and belongs to the technical field of investment decision-making in enterprise comprehensive planning management.
背景技术Background technique
现有的预测方法主要包括定性预测方法(市场调查预测法、专家预测法、主观概率法、预兆预测法等)、时间序列平滑预测法(指数平滑法、差分指数平滑法、自适应过滤法等)、回归模型(一元线性回归模型、多元线性回归模型、非线性回归模型等)、趋势外推预测法(指数曲线法、修正指数曲线法、生长曲线法、包络曲线法等)、马尔科夫预测法(多用于商品销售状态预测、市场占有率预测、期望利润预测)等。Existing forecasting methods mainly include qualitative forecasting methods (market survey forecasting method, expert forecasting method, subjective probability method, omen forecasting method, etc.), time series smoothing forecasting methods (exponential smoothing method, difference exponential smoothing method, adaptive filtering method, etc.) ), regression model (unary linear regression model, multiple linear regression model, nonlinear regression model, etc.), trend extrapolation prediction method (exponential curve method, modified exponential curve method, growth curve method, envelope curve method, etc.), Marko Husband forecast method (used for commodity sales status forecast, market share forecast, expected profit forecast) and so on.
综合比较以上预测分析方法，虽然回归模型是较为常见的预测方法，但它需要较大的样本容量作为支撑。一旦样本容量过小，预测结果很容易出现偏差。Comparing the above predictive analysis methods comprehensively, although the regression model is a relatively common forecasting method, it requires a large sample size as a support. Once the sample size is too small, the prediction results are prone to bias.
灰色预测由华中理工大学邓聚龙教授于1982年提出后，在社会、经济、科学技术等诸多领域得到了广泛应用，成为相关领域进行预测、决策、评估、规划控制、系统分析与建模的重要方法之一，它具有“建模信息少，运算方便，建模精度高”等优点。Gray prediction was proposed by Professor Deng Julong of Huazhong University of Science and Technology in 1982. It has been widely used in many fields such as society, economy, science and technology, and has become an important method for prediction, decision-making, evaluation, planning control, system analysis and modeling in related fields. One, it has the advantages of "less modeling information, convenient operation, high modeling accuracy".
发明内容Contents of the invention
本发明的目的在于克服现有技术存在的不足，而提供一种通过确定影响综合计划投入总盘的关键影响因素，并结合灰色预测方法和层次分析法，减少投资决策管理过程中的人为因素，确保预测过程更加科学、简便、高效，预测结果与实际情况更加一致的基于灰色理论的综合计划投入总盘预测方法。The purpose of the present invention is to overcome the deficiencies in the prior art, and provide a method to reduce the human factors in the investment decision-making management process by determining the key influencing factors that affect the comprehensive plan investment, and combining the gray forecasting method and the analytic hierarchy process. To ensure that the forecasting process is more scientific, simple and efficient, and the forecasting results are more consistent with the actual situation, it is a comprehensive planning investment forecasting method based on gray theory.
本发明的目的是通过如下技术方案来完成的，一种基于灰色理论的综合计划投入总盘预测方法，所述的预测方法包括如下步骤：The object of the present invention is to be accomplished through the following technical solutions, a kind of integrated planning based on the gray theory puts into the overall forecasting method, and described forecasting method comprises the following steps:
a)选择关键影响因素：从经济发展类因素、公司经营类因素、投资绩效类因素三个方面分析影响综合计划投入总盘可能存在哪些影响因素：并最终通过Excel表格的计算功能得到五个候选因素：营业收入、地区GDP、利润总额、售电量和用电最高负荷；a) Select the key influencing factors: analyze the possible influencing factors that may affect the investment in the comprehensive plan from three aspects: economic development factors, company operating factors, and investment performance factors: and finally get five candidates through the calculation function of the Excel table Factors: operating income, regional GDP, total profit, electricity sales and peak electricity load;
b)运用灰色预测方法来预测关键影响因素的变化趋势：借助MATLAB工具，按照灰色预测的算法步骤，编写相应的程序代码，运行程序之后便可得到地区GDP、全社会用电最高负荷、售电量、利润总额等关键影响因素未来的趋势值测算值；b) Use the gray forecast method to predict the changing trend of key influencing factors: with the help of MATLAB tools, according to the algorithm steps of gray forecast, write the corresponding program code, and after running the program, you can get the regional GDP, the highest load of electricity consumption in the whole society, and the electricity sales , total profit and other key influencing factors such as estimated future trend values;
c)确定关键影响因素的影响程度：借助层次分析法，综合定性分析和定量分析，将测算因素的定性排序转化为决策系数，再根据决策系数确定各因素变化对总盘投入的影响程度。c) Determine the degree of influence of key influencing factors: With the help of AHP, qualitative analysis and quantitative analysis are used to convert the qualitative ranking of measured factors into decision coefficients, and then determine the degree of influence of changes in various factors on the overall investment based on the decision coefficients.
主要步骤如下：(1)构造判断矩阵，根据“九标度法”确定各影响因素的重要性，并进行一致性检验；(2)计算决策系数，找出判断矩阵的最大特征值对应的特征向量，并进行归一化处理；(3)计算影响系数：由上述决策系数，计算各因素对综合计划投入影响程度的大小；The main steps are as follows: (1) construct a judgment matrix, determine the importance of each influencing factor according to the "nine scale method", and conduct a consistency test; (2) calculate the decision coefficient, and find out the characteristic corresponding to the largest eigenvalue of the judgment matrix (3) Calculation of influence coefficient: Calculate the degree of influence of each factor on the input of the comprehensive plan from the above decision coefficient;
d)预测综合计划投入规模：根据测算模型，按照以下公式可以得到综合计划投入年度预测结果：d) Forecasting the investment scale of the comprehensive plan: According to the calculation model, the annual forecast result of the comprehensive plan investment can be obtained according to the following formula:
上述公式中，Z表示计划年度的综合计划投入测算值；ui为第i个关键影响因素的影响系数(影响程度大小)，即因素i单位变化所对应的综合计划投入的变化量；xi(j+1)为第j+1年(计划年度)因素i的目标值；xi(j)为第j年因素i的实际值。In the above formula, Z represents the estimated value of comprehensive planning input in the planning year; u i is the influence coefficient (influence degree) of the i-th key influencing factor, that is, the change in comprehensive planning input corresponding to the unit change of factor i; x i (j+1) is the target value of factor i in year j+1 (planning year); x i (j) is the actual value of factor i in year j.
作为优选：所述的：步骤a)中，影响因素(1)经济发展类因素的选择是：初步确定地区GDP、全社会用电量、全社会用电最高负荷等3个因素。其中，地区GDP是反映地方经济发展趋势的重要指标，全社会用电量、全社会用电最高负荷反映的是区域电力需求的变动趋势，这些都是电力企业进行投入决策的重要影响因素；As a preference: said: in step a), the selection of influencing factors (1) economic development factors is: initially determine three factors such as regional GDP, electricity consumption of the whole society, and the highest load of electricity consumption in the whole society. Among them, regional GDP is an important indicator that reflects the development trend of the local economy. The electricity consumption of the whole society and the highest load of electricity consumption in the whole society reflect the changing trend of regional electricity demand. These are important factors affecting the investment decision-making of power companies;
影响因素(2)公司经营类因素的选择是：初步确定售电量、营业收入、资产总额、利润总额等4个因素。其中，售电量和“全社会用电量”关联极大，反映电力需求的变动趋势；营业收入、资产总额、利润总额则是反映公司经营现状和投资能力的重要指标；Influencing factors (2) The selection of the company's operating factors is to preliminarily determine four factors including electricity sales, operating income, total assets, and total profits. Among them, electricity sales are closely related to "power consumption of the whole society", reflecting the changing trend of electricity demand; operating income, total assets, and total profits are important indicators reflecting the company's operating status and investment capabilities;
影响因素(3)投资绩效类因素的选择是：初步确定净资产收益率、单位电网投资售电增量、单位电网投资负荷增量等3个因素。这些指标用以衡量公司投入产出的效益，在筛选影响因素时应纳入，毕竟电网企业也要考虑投资收益；Influencing factors (3) The selection of investment performance factors is: initially determine the return on net assets, the incremental power sales of unit grid investment, and the incremental investment load of unit grid. These indicators are used to measure the benefits of the company's input and output, and should be included in the screening of influencing factors. After all, power grid companies also need to consider investment returns;
通过对综合计划管理的相关历史数据进行相关性分析，直接剔除相关性相对较低的因素，进一步缩小范围，得到五个候选因素：营业收入、地区GDP、利润总额、售电量和用电最高负荷。Through the correlation analysis of the relevant historical data of comprehensive plan management, the factors with relatively low correlation are directly eliminated, and the scope is further narrowed to obtain five candidate factors: operating income, regional GDP, total profit, electricity sales and peak electricity load .
预测综合计划投入总盘，只要具备此前5年地区GDP、用电最高负荷、售电量和利润总额的基础数据，借助自行开发的实用软件，不需要10分钟即可得到下一年度综合计划投入总盘的预测数据。如有必要，可在自行开发的实用软件中，对相关数据进行调整(如改变判断矩阵中的数值、微调相关历史数据等)，快速得到新的预测结果，非常方便实用。To predict the total investment of the comprehensive plan, as long as you have the basic data of regional GDP, peak power consumption, electricity sales and total profit in the previous five years, with the help of self-developed practical software, you can get the total investment of the comprehensive plan for the next year in less than 10 minutes. Disk forecast data. If necessary, the relevant data can be adjusted in the self-developed practical software (such as changing the value in the judgment matrix, fine-tuning the relevant historical data, etc.), and quickly get new prediction results, which is very convenient and practical.
总体上，本发明在减少人员工作量的基础上，提高了综合计划投入总盘预测的准确性和科学性。On the whole, the present invention improves the accuracy and scientificity of the comprehensive plan investment total disk forecast on the basis of reducing the workload of personnel.
具体实施方式detailed description
下面将结合具体实施例对本发明作详细的介绍：本发明所述的基于灰色理论的综合计划投入总盘预测方法，它主要包括如下步骤：The present invention will be described in detail below in conjunction with specific embodiments: the integrated planning based on gray theory described in the present invention puts into the total disk prediction method, and it mainly comprises the steps:
a)选择关键影响因素：a) Select key influencing factors:
首先，从三个方面分析影响综合计划投入总盘可能存在哪些影响因素：First of all, analyze the factors that may affect the total investment of the comprehensive plan from three aspects:
(1)经济发展类因素：初步确定地区GDP、全社会用电量、全社会用电最高负荷等3个因素。其中，地区GDP是反映地方经济发展趋势的重要指标，全社会用电量、全社会用电最高负荷反映的是区域电力需求的变动趋势，这些都是电力企业进行投入决策的重要影响因素。(1) Economic development factors: initially determine three factors including regional GDP, electricity consumption of the whole society, and the highest load of electricity consumption in the whole society. Among them, regional GDP is an important indicator reflecting the development trend of the local economy. The electricity consumption of the whole society and the highest load of electricity consumption in the whole society reflect the changing trend of regional electricity demand. These are important factors affecting the investment decision-making of power companies.
(2)公司经营类因素：初步确定售电量、营业收入、资产总额、利润总额等4个因素。其中，售电量和“全社会用电量”关联极大，反映电力需求的变动趋势；营业收入、资产总额、利润总额则是反映公司经营现状和投资能力的重要指标。(2) Factors of company operation: initially determine four factors including electricity sales, operating income, total assets, and total profits. Among them, electricity sales are closely related to "power consumption of the whole society", reflecting the changing trend of electricity demand; operating income, total assets, and total profits are important indicators reflecting the company's operating status and investment capabilities.
(3)投资绩效类因素：初步确定净资产收益率、单位电网投资售电增量、单位电网投资负荷增量等3个因素。这些指标用以衡量公司投入产出的效益，在筛选影响因素时应纳入，毕竟电网企业也要考虑投资收益。(3) Investment performance factors: Initially determine three factors including return on net assets, incremental electricity sales per unit grid investment, and incremental unit grid investment load. These indicators are used to measure the benefits of the company's input and output, and should be included in the screening of influencing factors. After all, grid companies also need to consider investment returns.
其次，通过对综合计划管理的相关历史数据进行相关性分析，直接剔除相关性相对较低的因素，进一步缩小范围，得到五个影响因素：营业收入、地区GDP、利润总额、售电量和用电最高负荷。这一步主要通过Excel表格的计算功能实现。具体操作：新建Excel表单，输入相关原始数据，点击“数据分析——分析工具——相关系数”，即可进行综合计划投入总盘与候选影响因素的相关性计算。Secondly, through the correlation analysis of the relevant historical data of comprehensive plan management, the factors with relatively low correlation are directly eliminated, and the scope is further narrowed, and five influencing factors are obtained: operating income, regional GDP, total profit, electricity sales and electricity consumption maximum load. This step is mainly realized through the calculation function of the Excel table. Specific operation: Create a new Excel sheet, enter relevant raw data, and click "Data Analysis-Analysis Tools-Correlation Coefficient" to calculate the correlation between the total planned investment and the candidate influencing factors.
本发明的另一个实施例也可以在上述五个影响因素的基础上，对经过相关性计算得出的5个相关性较高的候选影响因素进行深入分析，从中最终确定关键影响因素为四个，即地区GDP、用电最高负荷、售电量和利润总额。这四个关键因素实际上从满足社会经济发展、保障电网坚强、展现公司经营成果和经营绩效等四个不同的侧面，共同制约综合计划的投入规模。In another embodiment of the present invention, on the basis of the above-mentioned five influencing factors, an in-depth analysis can be carried out on the five highly correlated candidate influencing factors obtained through correlation calculation, from which the key influencing factors are finally determined to be four , that is, regional GDP, peak power load, electricity sales and total profit. In fact, these four key factors jointly restrict the investment scale of the comprehensive plan from four different aspects of satisfying social and economic development, ensuring the strength of the power grid, and showing the company's operating results and operating performance.
b)运用灰色理论进行预测：b) Prediction using gray theory:
实际操作需要借助MATLAB工具，按照灰色预测的算法步骤，编写相应的程序代码，运行程序之后便可得到地区GDP、全社会用电最高负荷、售电量、利润总额等关键影响因素未来的趋势值测算值。The actual operation requires the use of MATLAB tools to write the corresponding program code according to the algorithm steps of gray forecasting. After running the program, the future trend value calculation of key influencing factors such as regional GDP, the highest load of electricity consumption in the whole society, electricity sales, and total profit can be obtained. value.
c)确定关键影响因素的影响程度：c) Determine the degree of influence of key influencing factors:
为了确定五个或四个关键影响因素对综合计划投入总盘的影响程度，借助层次分析法，综合定性分析和定量分析，将测算因素的定性排序(根据专家经验，将五个或四个关键影响因素的重要性进行排序)转化为决策系数，再根据决策系数确定各因素变化对总盘投入的影响程度。In order to determine the degree of influence of five or four key factors on the overall investment plan, with the help of analytic hierarchy process, comprehensive qualitative analysis and quantitative analysis, the qualitative ranking of the measurement factors (according to expert experience, the five or four key Ranking the importance of influencing factors) into a decision coefficient, and then according to the decision coefficient to determine the degree of influence of each factor change on the overall investment.
主要步骤如下：(1)构造判断矩阵，根据“九标度法”确定各影响因素的重要性，并进行一致性检验；(2)计算决策系数，找出判断矩阵的最大特征值对应的特征向量，并进行归一化处理；(3)计算影响系数：由上述决策系数，计算各因素对综合计划投入影响程度的大小。The main steps are as follows: (1) construct a judgment matrix, determine the importance of each influencing factor according to the "nine scale method", and conduct a consistency test; (2) calculate the decision coefficient, and find out the characteristic corresponding to the largest eigenvalue of the judgment matrix (3) Calculation of influence coefficient: From the above decision coefficient, calculate the degree of influence of each factor on the input of the comprehensive plan.
d)预测综合计划投入规模：d) Predict the investment scale of the comprehensive plan:
根据测算模型，按照以下公式可以得到综合计划投入年度预测结果：According to the calculation model, the annual forecast results of comprehensive plan investment can be obtained according to the following formula:
上述公式中，Z表示计划年度的综合计划投入测算值；ui为第i个关键影响因素的影响系数(影响程度大小)，即因素i单位变化所对应的综合计划投入的变化量；xi(j+1)为第j+1年(计划年度)因素i的目标值；xi(j)为第j年因素i的实际值。In the above formula, Z represents the estimated value of comprehensive planning input in the planning year; u i is the influence coefficient (influence degree) of the i-th key influencing factor, that is, the change in comprehensive planning input corresponding to the unit change of factor i; x i (j+1) is the target value of factor i in year j+1 (planning year); x i (j) is the actual value of factor i in year j.
实施例：根据灰色预测GM(1,1)模型，借助MATLAB程序，2016年浙江地区GDP的预测值为46566亿元，全社会用电最高负荷预测值为6617万千瓦，售电量预测值为2004亿千瓦时，利润总额预测值为38.2亿。同时推算出关键影响因素的影响系数分别为：地区GDP为0.053，全社会用电最高负荷为0.39，售电量为0.37，利润总额为5.28。根据总盘投入测算公式，推知浙江公司2016年投入总盘测算值为328亿元，这与上级公司下达的300亿元的投资额度非常接近。Example: According to the gray prediction GM(1,1) model, with the help of MATLAB program, the predicted value of GDP in Zhejiang region in 2016 is 4,656.6 billion yuan, the predicted value of the highest load of electricity consumption in the whole society is 66.17 million kilowatts, and the predicted value of electricity sales is 2004 100 million kwh, the total profit forecast value is 3.82 billion. At the same time, the influence coefficients of key influencing factors are calculated as follows: regional GDP is 0.053, the highest load of electricity consumption in the whole society is 0.39, electricity sales is 0.37, and total profit is 5.28. According to the calculation formula of the total investment, it is inferred that the estimated value of the total investment of Zhejiang Company in 2016 is 32.8 billion yuan, which is very close to the investment quota of 30 billion yuan issued by the superior company.
Claims (2)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201511017465.9A CN105654197A (en) | 2015-12-29 | 2015-12-29 | Grey theory-based comprehensive plan investment total prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201511017465.9A CN105654197A (en) | 2015-12-29 | 2015-12-29 | Grey theory-based comprehensive plan investment total prediction method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105654197A true CN105654197A (en) | 2016-06-08 |
Family
ID=56478186
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201511017465.9A Pending CN105654197A (en) | 2015-12-29 | 2015-12-29 | Grey theory-based comprehensive plan investment total prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105654197A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106485367A (en) * | 2016-10-26 | 2017-03-08 | 贵州电网有限责任公司电力科学研究院 | A kind of economic analysis platform based on the coupling of multiple enterprises electricity consumption data and Forecasting Methodology |
| CN110796365A (en) * | 2019-10-28 | 2020-02-14 | 国网能源研究院有限公司 | A Decision-Making Method for International Power Grid Input Based on Electricity Demand Forecast |
| CN110793896A (en) * | 2019-12-03 | 2020-02-14 | 承德石油高等专科学校 | Short-term prediction method for dust concentration in tail gas |
-
2015
- 2015-12-29 CN CN201511017465.9A patent/CN105654197A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106485367A (en) * | 2016-10-26 | 2017-03-08 | 贵州电网有限责任公司电力科学研究院 | A kind of economic analysis platform based on the coupling of multiple enterprises electricity consumption data and Forecasting Methodology |
| CN110796365A (en) * | 2019-10-28 | 2020-02-14 | 国网能源研究院有限公司 | A Decision-Making Method for International Power Grid Input Based on Electricity Demand Forecast |
| CN110793896A (en) * | 2019-12-03 | 2020-02-14 | 承德石油高等专科学校 | Short-term prediction method for dust concentration in tail gas |
| CN110793896B (en) * | 2019-12-03 | 2022-04-08 | 承德石油高等专科学校 | Short-term prediction method for dust concentration in tail gas |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hilorme et al. | Formation of risk mitigating strategies for the implementation of projects of energy saving technologies | |
| Ai et al. | Electricity price and industrial green productivity: Does the “low-electricity price trap” exist? | |
| CN105868839A (en) | Emergency material reservation optimization system | |
| Izadikhah et al. | Developing a new chance constrained NDEA model to measure performance of sustainable supply chains | |
| CN103413254A (en) | Medium-and-long-term load prediction research and management integration application system | |
| CN106447198A (en) | Power consumption checking method based on business expanding installation data | |
| CN106447108A (en) | Power utilization demand analysis prediction method taking business-expansion installation data into consideration | |
| CN116823008A (en) | Park energy utilization efficiency evaluation method, system, equipment and storage medium | |
| CN109447332A (en) | A kind of Middle-long Electric Power Load Forecast method suitable for S type load curve | |
| CN115640876A (en) | Electric power energy carbon emission prediction method based on improved STIRPAT model | |
| CN105654197A (en) | Grey theory-based comprehensive plan investment total prediction method | |
| KR101245650B1 (en) | System and Method for quantitatively predicting and evaluating the result of Research/Development Technology | |
| CN113723842A (en) | Research and development expense investment scale and strength prediction method | |
| CN104463366A (en) | Method for analyzing and optimizing electric grid investment scale | |
| CN103077435A (en) | SEC (Securities and Exchange Commission) index evaluation method based on combination weighting comprehensive evaluation model | |
| CN106204098A (en) | A kind of whole world underwear industry data is collected and analysis platform and the method for analysis thereof | |
| Vedernikov et al. | Modern Aspects of Industrial Enterprises’ Production Efficiency Management | |
| Wang et al. | Regional eco-efficiency prediction with support vector spatial dynamic MIDAS | |
| Zhu et al. | Evaluation of the integration of industrialization and information-based entropy AHP–cross-efficiency DEA model | |
| Wu et al. | Research on the performance measurement of knowledge management based on principal component analysis | |
| Wei et al. | Research and Development Investment Combination Forecasting Model of High‐Tech Enterprises Based on Uncertain Information | |
| CN110837969A (en) | Evaluation and prediction method of input-output benefit of distribution network based on peer-to-peer measurement model | |
| Fawait et al. | Implementation of Data Mining Using Simple Linear Regression Algorithm to Predict Export Values | |
| Yemelyanov et al. | Information Support for Assessing the Potential of Implementing Resource-Saving Technological Changes in Enterprises | |
| Jiang | Neural network-based venture capital risk assessment model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20160608 |
|
| WD01 | Invention patent application deemed withdrawn after publication |