CN112085364A - Business cause-based company economic activity prediction method - Google Patents
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Abstract
The invention relates to a business cause-based company economic activity prediction method, which comprises the following steps of: 1) business index prediction influencing electricity selling quantity; 2) constructing a power selling quantity change prediction model based on the business cause; 3) predicting the profit list; 4) forecasting an asset balance sheet; 5) predicting a cash flow table; 6) and measuring and calculating the main performance indexes based on the predicted profit statement, the predicted cash flow statement and the predicted asset liability statement. When the invention carries out the pre-judgment analysis of the operation performance, the prediction is carried out from the action of the bottom business, the measuring, calculating and analyzing basis is tamped, the invention is more widely combined with the actual production and the operation activities, and the depth and the breadth of the operation analysis are deepened. Meanwhile, by smoothing the quantitative relation among the indexes, the automatic measurement and calculation analysis of the prediction process index and the performance index based on basic data and parameters is realized, and the efficiency and the accuracy of the economic activity analysis are improved. The invention can be widely applied to the field of power grid operation management.
Description
Technical Field
The invention relates to a business cause-based company economic activity prediction method, and belongs to the field of power grid operation management.
Background
Influenced by the descending of macroscopic economy and the control of epidemic situation, the social production pace is slowed down, and the electric quantity increase income is further narrowed. In order to represent social responsibility of the central enterprises, the power grid company continuously reduces the power price, and the uncertainty of company operation is increased. Therefore, early warning of the operation risk of the company is of great significance for timely finding the problems of various operation activities of the company and providing corresponding measures.
Economic activity analysis, which is centered on value creation, is facing more challenges: firstly, the current economic activity analysis focuses on the analysis of the current situation of the operation result, the prediction of future operation potential and risk is lacked, and the risk monitoring and early warning functions of the economic activity analysis are not well exerted; secondly, the current economic activity analysis work is lack of decomposition of business change and excavation of deep reasons, and the economic activity analysis depth is not enough. Therefore, by combing key indexes of key business activities at the bottom layer, the business activity indexes are predicted based on a mathematical algorithm, an economic activity prediction model is constructed, the analysis of the economic activities of the company and the prediction of the operation performance under multiple scenes are supported, and the operation decision and risk early warning of the company are assisted.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method for predicting economic activities of companies based on business causes, which can scientifically and quantitatively predict the economic activities of the companies.
In order to achieve the purpose, the invention adopts the following technical scheme: a business incentive-based company economic activity prediction method comprises the following steps:
1) business index prediction influencing electricity selling quantity;
2) constructing a power selling quantity change prediction model based on the business cause;
3) predicting the profit list;
4) forecasting an asset balance sheet;
5) predicting a cash flow table;
6) and measuring and calculating the main performance indexes based on the predicted profit statement, the predicted cash flow statement and the predicted asset liability statement.
Further, in step 1), the service index affecting the electricity selling amount includes: and the business expansion installs new capacity, electric energy replacing electric quantity, failure first-aid repair loss electric quantity and distributed clean energy self-use electric quantity.
Further, in step 1), the service index prediction affecting the electricity selling amount includes the following steps:
1.1) developing new capacity prediction of business expansion equipment based on a regression method;
1.2) carrying out electric energy substitution electric quantity prediction by constructing a combined prediction model;
1.3) carrying out power failure loss electric quantity prediction through an XGB OST algorithm;
1.4) carrying out self-electricity utilization prediction of the distributed clean energy based on a regression method.
Further, in the step 1.1), when predicting the new capacity of the business expansion installation based on the regression method, firstly, performing time difference correlation analysis based on the data of the new capacity of the historical business expansion installation and the new electric quantity of the business expansion installation, and grasping the hysteresis relationship between the new electric quantity and the new capacity; secondly, data preprocessing is carried out based on the analyzed hysteresis relation, regression analysis is carried out on the processed data to construct the quantitative relation between the future newly added electric quantity and the historical newly added capacity, and therefore prediction of the newly added capacity of the business expansion device is achieved.
Further, in the step 1.4), when the self-electricity consumption of the clean energy is predicted: firstly, user data of self-use of distributed clean energy sources are combed, the self-use data of the distributed clean energy sources in the users are collected and sorted, or the data are inversely calculated based on plant utilization rate and internet electricity quantity, and the number of the clients is counted; secondly, analyzing the relation between the self-electricity consumption of the distributed clean energy of the single user and electricity price, subsidy scale and seasonal weather, constructing a regression model, and predicting the self-electricity consumption scale of the distributed clean energy of the single user; thirdly, analyzing the correlation between the number of the self-use users of the distributed clean energy and the total number of the users, policies and electricity prices, constructing a regression prediction model, and predicting the number of the users of the distributed clean energy; and finally, calculating the self-consumption electric quantity of the clean energy in the prediction period based on the predicted self-consumption electric quantity of the single-user distributed clean energy and the number of the distributed clean energy users.
Further, in the step 2), the method for constructing the power-selling amount change prediction model based on the business cause includes the following steps:
2.1) calculating the base value of the predicted period electricity sales amount based on the historical electricity sales amount and the average growth rate, namely
The predicted period electricity selling base value is the actual value of the last period electricity selling (1+ the average electricity selling increase rate of the last 3 years);
2.2) considering the forecast change value of the newly-increased capacity of the business expansion installation, the electric energy replacing electric quantity, the failure emergency repair loss electric quantity and the distributed clean energy self-use electric quantity, adjusting the basic value of the electricity selling quantity in the forecast period as the forecast electricity selling quantity, namely:
the predicted electricity sales amount is the predicted electricity sales amount basic value, the business expansion new capacity increasing electricity amount variation value, the electric energy replacing electricity amount, the fault first-aid repair loss electricity amount variation value and the distributed clean energy self-use electricity amount variation value.
Further, in the step 3), the method for predicting the profit margin includes the following steps:
3.1) combing the income incentive value index parameters of the profit schedule, and determining the influence ways and the quantitative logics of the income on the income by the incentive value index parameters based on the service characteristics and the historical data characteristics;
3.2) combing the profit list cost factor value index parameters, and determining the influence ways and quantitative logics of the factor value index parameters on the cost based on the service characteristics and the historical data characteristics;
3.3) deducting cost items by using income items and calculating total profit; deducting the obtained tax to calculate net profit;
further, in the step 4), the method for predicting the balance sheet includes the following steps:
4.1) combing asset dynamic factor value index parameters, and determining the influence ways and quantitative logics of the dynamic factor value index parameters on the assets based on the service characteristics and the historical data characteristics;
4.2) combing the liability and cause value index parameters, and determining the influence ways and the quantitative logic of each cause value index parameter on the assets based on the business characteristics and the historical data characteristics;
4.3) developing various equity predictions based on the owner equity change logic.
Further, in the step 5), the method for predicting the cash flow table includes the following steps:
5.1) carrying out prediction on the cash flow table of the operation activities;
the cash flow of the operation activity is net profit, financial expense, depreciation, amortization, operation capital change, non-cash investment income of long-term equity investment, long-term receivable reduction, other non-liquidity assets reduction, long-term receivable increase, long-term payable salary increase, special receivable increase, delayed income increase and other non-liquidity liability increase;
5.2) carrying out prediction on the cash flow of investment activities;
investment activity cash flow is fixed asset investment, fixed asset income disposal and fixed asset value reduction preparation increase, long-term equity investment and intangible asset investment;
5.3) carrying out prediction on cash flow of the financing activities;
the cash flow of the financing activity is equal to the increase of the interest bearing debt, the decrease of the interest bearing debt, the financial cost and the increase of the interest to be paid, the capital income of the paying country, the increase of the bond to be paid and the subsidy of the national fund;
5.4) capital gap measurement and calculation;
fund gap-lowest cash balance-initial fund balance-cash inflow in this period in addition to financing activities + cash outflow in this period.
Further, in the step 6), the method for measuring and calculating the main performance index based on the predicted profit statement, the predicted cash flow statement and the predicted asset liability statement comprises the following steps:
6.1) determining main operation performance indexes to be measured and calculated, including gross profit, asset liability rate, EVA and net asset profitability;
6.2) measuring and calculating each determined performance index;
wherein the total profit amount is the total profit amount from the exterior to the interior;
the rate of assets liability is equal to liability/total amount of assets;
EVA (post-tax net operating profit-capital cost);
wherein, the net operating profit after tax is net profit + (interest expenditure + research and development fee) × (1-income tax); capital cost ═ (average owner equity + average interest bearing-average under construction) × capital cost rate, where capital cost rate ═ bond capital cost rate × interest bearing x (1-derived tax rate)/(average owner equity + average interest bearing) + equity capital cost rate × average owner equity/(average owner equity + average interest bearing).
Due to the adoption of the technical scheme, the invention has the following advantages: 1. when the invention carries out the pre-judgment analysis of the operation performance, the prediction is carried out from the action of the bottom business, the measuring, calculating and analyzing basis is tamped, the invention is more widely combined with the actual production and the operation activities, and the depth and the breadth of the operation analysis are deepened. 2. According to the method, the quantitative relation among the indexes is straightened, the automatic measurement and calculation analysis of the prediction process index and the performance index based on basic data and parameters is realized, the efficiency and the accuracy of economic activity analysis are improved, and the dimensionality of business analysis is expanded. Therefore, the invention can be widely applied to the field of power grid operation management.
Detailed Description
The present invention will be described in detail with reference to examples.
The invention provides a business cause-based company economic activity prediction method, which comprises the following steps of:
1) business index prediction influencing electricity selling quantity.
Specifically, the method and the device have the advantages that the main services influencing the electricity selling quantity are combed on the basis of importance, data availability and quantification, and the main services comprise business expansion new capacity, electric energy replacing quantity, failure emergency repair lost quantity and distributed clean energy self-use quantity.
1.1) developing new capacity prediction of business expansion equipment based on a regression method.
Because the electric quantity increase has a hysteresis effect compared with the business expansion time, the business expansion new capacity prediction is developed based on the historical business expansion new capacity and the new electric quantity selling data. Firstly, performing time difference correlation analysis based on historical business expansion newly-added capacity and business expansion newly-added electric quantity data, and grasping the hysteresis relation of newly-added electric quantity to newly-added capacity; secondly, data preprocessing is carried out based on the analyzed hysteresis relation, regression analysis is carried out on the processed data to construct the quantitative relation between the future newly added electric quantity and the historical newly added capacity, and therefore prediction of the newly added capacity of the business expansion device is achieved.
1.2) carrying out electric energy substitution electric quantity prediction by constructing a combined prediction model (a neural network, a support vector machine, regression and the like).
The electric energy replacing electric quantity is mainly influenced by factors such as economic development, environmental protection constraint, energy price, policy support, technical progress and the like. Economic development is measured by the total production value of per capita regions, environmental protection constraint is measured by unit GDP pollutant emission, energy price is measured by fuel price index, policy support is measured by the fixed asset investment ratio of the industry, and technical progress is measured by unit GDP energy consumption. Based on the electric energy substitution key influence factors, electric energy substitution electric quantity prediction is respectively carried out through prediction methods such as a neural network, a support vector machine and regression, a combined prediction model is constructed by taking the reciprocal of prediction deviation as weight, and the stability of prediction effect is improved.
1.3) carrying out power failure loss electric quantity prediction through an XGB OST algorithm.
Based on historical fault power failure data, power failure loss electric quantity data of a company and weather characteristic data such as the highest temperature, the lowest temperature, weather, wind power, wind direction and the like, power failure loss electric quantity prediction is carried out through an XGBOOST algorithm. The XGBoost is a boosting method, which is very similar to the traditional gradient lifting tree, except that the complexity of the tree is defined on the basis of the traditional lifting tree, and the tree is split into a structural part and a leaf weight part.
1.4) carrying out self-electricity utilization prediction of the distributed clean energy based on a regression method.
The self-electricity consumption of the distributed clean energy is related to the self-electricity consumption of the distributed clean energy of a single user and the total number of users of the distributed clean energy. The self-electricity consumption of the clean energy of a single user is related to factors such as user category, subsidy scale and seasonal weather, and the total number of the users is related to factors such as electricity price level and policy strength.
When the self-electricity consumption of the clean energy is predicted: firstly, combing user data of self-use of distributed clean energy, collecting and sorting self-use electricity data (or performing inverse calculation based on plant utilization rate and internet electricity quantity data) of the distributed clean energy sources in the users, and counting the number of the clients; secondly, analyzing the relation between the self-electricity consumption of the distributed clean energy of the single user and electricity price, subsidy scale, seasonal weather and the like, constructing a regression model, and predicting the self-electricity consumption scale of the distributed clean energy of the single user; thirdly, analyzing the correlation between the number of the self-use users of the distributed clean energy and factors such as the total number of users, policies, electricity prices and the like, constructing a regression prediction model, and predicting the number of the users of the distributed clean energy; and finally, calculating the self-consumption electric quantity of the clean energy in the prediction period based on the predicted self-consumption electric quantity of the single-user distributed clean energy and the number of the distributed clean energy users.
2) And constructing a power selling amount change prediction based on the business cause.
Specifically, the method comprises the following steps:
and 2.1) calculating a base value of the electricity sales in the prediction period based on the historical electricity sales and the average growth rate.
Namely: the predicted electricity selling base value is the actual value of the electricity selling in the upper period (1+ the average electricity selling increase rate in the last 3 years).
And 2.2) adjusting a basic value of the electricity selling quantity in the prediction period to be used as the predicted electricity selling quantity by considering the forecast change value of the newly increased capacity of the business expansion installation, the electric energy replacing quantity, the failure emergency repair loss quantity and the distributed clean energy self-consumption quantity.
Namely:
the predicted electricity sales amount is the predicted electricity sales amount basic value, the business expansion new capacity increasing electricity amount variation value, the electric energy replacing electricity amount, the fault first-aid repair loss electricity amount variation value and the distributed clean energy self-use electricity amount variation value.
3) The method for constructing the profit list comprises the following steps:
and 3.1) combing the income incentive value index parameters of the profit schedule, and determining the influence ways and quantitative logics of the income on the incentive value index parameters on the basis of the business characteristics and the historical data characteristics.
The net business income is the business income of the main operation of the electric power product, the business income of other main operations and the business income of other operations.
The method comprises the following steps that the main operation income of the electric power product is electricity selling income, power transmission income, high-reliability power supply income, standby expense income of a self-contained power plant system, trusted operation maintenance income, temporary electricity receiving income, renewable energy price addition and main operation income of other electric power products;
wherein, the electricity selling income is the electricity selling quantity multiplied by the electricity selling price; and (4) the business income of other electric power products is the last year value plus the forecast period change adjustment value.
The price of the electricity sold is the price of the electricity sold in the last year plus the adjustment amount of the forecast period.
And 3.2) combing the profit list cost factor value index parameters, and determining the influence ways and quantitative logics of the factor value index parameters on the cost based on the service characteristics and the historical data characteristics.
The total business cost is business cost + tax and additional + sales cost + management cost + financial cost + asset value loss; wherein, the business cost is the business cost of the electric power product, the other business cost and the other business cost
The main operation cost of the electric power product is the electricity purchasing cost, the power transmission and distribution cost and the power generation cost
The electricity purchase cost is the electricity sale quantity/(1-line loss rate) multiplied by electricity purchase price, and the electricity purchase price is the electricity purchase price in the previous period plus the forecast period adjustment value.
Power transmission and distribution cost, depreciation, labor cost, material repair cost and other operation cost
Wherein, depreciation is the newly added investment scale, the capital payment proportion, the conversion rate and the depreciation rate
The labor cost is the wage, social insurance, wage addition, employee welfare and housing accumulation fund
Wherein, the payroll is the cost of the payroll in the previous period x (1+ payroll acceleration rate) + the adjustment amount of the payroll in the prediction period
Other labor costs are upper value x (1+ payroll acceleration rate) + predicted adjustment amount
Material repair fee/other operation fee upper period value + forecast period increment + forecast adjustment amount
The power generation cost is depreciation cost, water cost, labor cost, maintenance cost and other operation cost;
the labor cost is the cost of the payroll in the previous period x (1+ payroll acceleration rate) + the adjustment amount of the payroll in the prediction period;
the other costs are averaged over the first three years.
The tax and the additional total income net sum of the tax and the additional operating income proportion, wherein the tax and the additional operating income proportion are averaged in 3 years.
Sale expense and management expense are the scale of the upper period multiplied by the growth rate of the upper period
The financial cost (initial debt balance + current period new financing-current period paid debt) x average financing cost, wherein the current period new financing is a fund gap calculated through loop iteration.
Asset loss-last year value + forecast period adjustment value.
3.3) deducting cost items by using income items and calculating total profit; and deducting the income tax to calculate net profit.
The business profit is the net business income, the total business cost, the allowed value change income, the investment income, the asset disposal income and other income;
wherein, the public allowable value change income, investment income, asset disposition income and other income are taken as the average value of the calendar history of nearly 3.
The total profit is business profit + external revenue-external cost;
wherein the business income is fixed asset income, default use fee, metering device compensation fee, non-currency asset exchange, previous annual sum of money cannot be paid and other income; the external expenditure of the operation is fixed asset disposal loss, public welfare donation and others; and (4) taking account of the measurement of the adjustment amount of the forecast period on the basis of the last year value of each business balance.
Net profit (total profit) (1-income tax rate), wherein the income tax rate is set as the average of the proportion of income tax to the total profit in the last 3 years.
4) The method for constructing the forecast of the balance sheet specifically comprises the following steps:
4.1) combing the asset dynamic value index parameters, and determining the influence ways and quantitative logics of the dynamic value index parameters on the assets based on the service characteristics and the historical data characteristics.
Taking the cash flow table end balance from the monetary fund;
the accounts receivable and the bill are the accounts receivable turnover rate × business income, wherein the accounts receivable turnover rate is set according to the historical 3-year average value.
The prepayment term is fixed asset investment cash expenditure multiplied by the proportion of prepayment term in fixed asset investment expenditure, wherein the proportion of prepayment term in fixed asset investment expenditure is set according to the average value of 3 years in history.
The ratio of the stock to the material charge and the repair charge is multiplied by the material repair charge, and the ratio of the stock to the material charge and the repair charge is set according to the average value of the three years.
Long-term equity investment, which is the up-term offerable financial asset x (1+ long-term equity investment and offerable financial asset investment profitability) -the current-term equity investment expenditure, wherein the long-term equity investment and offerable financial asset investment profitability are the up-term equity investment and offerable financial asset investment profit/(up-term equity investment + up-term offerable financial asset).
Fixed asset original value + initial fixed asset original value + present investment x investment cash expenditure proportion x investment transfer rate-fixed asset reduction amount
The accumulated depreciation of the fixed assets is the accumulated depreciation of the fixed assets at the beginning of the period, the newly increased depreciation of the period and the corresponding depreciation of the fixed assets reduced at the period;
the net fixed asset value is the original fixed asset value and accumulated depreciation;
net fixed asset-net fixed asset derate preparation.
In the initial construction project in the first construction project period + investment in the current period x cash expenditure proportion of investment-capital investment transfer in the last year-capital investment transfer in the current year-capital investment transfer-capitalization interest;
other assets are initial values;
4.2) combing the liability and cause value index parameters, and determining the influence ways and the quantitative logic of the cause value index parameters on the assets based on the business characteristics and the historical data characteristics. Liabilities are divided into charged liabilities and non-charged liabilities.
4.2.1) interest bearing liability includes short term loans, long term loans due within a year, long term loans and bonds.
Short term borrowing is equal to initial short term borrowing in the future + total fund gap x short term borrowing financing proportion-repayment part in the forecast period
Long term borrowing due within one year, long term borrowing due within the first year in the beginning of the period and long term borrowing transferred to the part due within one year-the part refunded within the current year
Long term borrowing, initial long term borrowing, fund gap, long term borrowing financing proportion, and transferring into the part due within one year in the long term borrowing
Bond initial bond + total fund gap x bond financing ratio-term repayment part.
4.2.2) non-interest liability including accounts payable and bills, pre-paid terms, employee payable, taxes payable, interest payable, etc.
Payable and bill are the proportion of payable and bill to total business cost x total business cost
Pre-paid account is the ratio of pre-paid account to business income multiplied by business income
Payable staff compensation is the historical 3-year average + forecast period adjustment amount
Tax due to charge is Bix (total income net amount-business cost) accounting for gross business profit
Financial cost x ratio of interest to financial cost
Initial value of other debt period + adjustment amount of prediction period
4.3) developing various equity predictions based on the owner equity change logic.
Owner's equity belonging to parent owner's equity + few stakeholders ' equity
The equity of the owner belonging to the parent company is real income capital, other integrated income, the converted difference of the foreign currency report, the common product of the capital, the surplus common product, the unallocated profit and other
Wherein, the capital accumulation is the initial value in term, the financial assets available for sale, the investment earning rate of the financial assets available for sale, the national fund subsidy, the called-in and called-out assets
First-term profit-profit product + (net profit-paid national capital income belonging to mother company) x profit-profit product extraction ratio
Unallocated profit (initial unallocated profit of the mother company-capital profit of the paying country) × (1-profit-product extraction ratio)
Other owner's right to be equal to the initial value
Equity of few stockholders ═ initial value + net profit attributed to few stockholders
5) The method for constructing the cash flow table prediction comprises the following steps:
5.1) operating activity cash flow is net profit + financial cost + depreciation + amortization + operating capital change-non-cash investment income of long term equity investment + long term receivable decrease + other non-liquidity decrease + long term receivable increase + long term payable employee pay increase + special payable increase + delayed income increase + other non-liquidity increase
Wherein, the operating capital changes (the current liquidity-the current liquidity) - (the current liquidity-the current liquidity)
5.2) investment Activity cash flow-fixed asset investment + fixed asset earning + fixed asset derating preparation increase-Long term equity investment-intangible asset investment
Wherein, the income of the fixed asset is retired fixed asset multiplied by the residual rate
5.3) financing activity cash flow-interest burden decrease amount-financial cost + interest due increase-paying national capital gain + bond due increase + national capital fund subsidy
Wherein, the capital income of the paying nation is equal to profit of the last-stage attributive mother company x (1-legal public accumulation fund counting and drawing proportion) x payment proportion
5.4) end-of-term cash balance-initial cash balance + net business flow + net investment flow + net financing flow
5.5) Fund gap-lowest Cash balance-initial Fund balance-Cash inflow + Cash outflow except for financing Activity during this period, when Fund gap is less than 0, no gap exists
6) The method is characterized in that the main performance indexes are measured and calculated based on predicted profit tables, cash flow tables and asset liability tables, and specifically comprises the following steps:
6.1) the gross profit is the gross profit of the exterior and interior of the profit;
6.2) the rate of the assets liability is equal to the liability/total amount of the assets;
6.3) EVA net operating profit-capital cost after tax, wherein the net operating profit after tax is net profit + (interest expenditure + development fee) × (1-income tax); capital cost ═ (average owner equity + average interest bearing-average under construction) × capital cost rate, where capital cost rate ═ bond capital cost rate × interest bearing x (1-derived tax rate)/(average owner equity + average interest bearing) + equity capital cost rate × average owner equity/(average owner equity + average interest bearing).
The above embodiments are only used for illustrating the present invention, and the structure, connection mode, manufacturing process, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.
Claims (10)
1. A business cause-based company economic activity prediction method is characterized by comprising the following steps:
1) business index prediction influencing electricity selling quantity;
2) constructing a power selling quantity change prediction model based on the business cause;
3) predicting the profit list;
4) forecasting an asset balance sheet;
5) predicting a cash flow table;
6) and measuring and calculating the main performance indexes based on the predicted profit statement, the predicted cash flow statement and the predicted asset liability statement.
2. The business incentive based economic activity forecasting method for companies as claimed in claim 1, wherein the business indicators influencing the electricity sales in step 1) comprise: and the business expansion installs new capacity, electric energy replacing electric quantity, failure first-aid repair loss electric quantity and distributed clean energy self-use electric quantity.
3. The business incentive based economic activity prediction method for companies as claimed in claim 2, wherein the step 1) of predicting the business indexes affecting the electricity sales comprises the following steps:
1.1) developing new capacity prediction of business expansion equipment based on a regression method;
1.2) carrying out electric energy substitution electric quantity prediction by constructing a combined prediction model;
1.3) carrying out power failure loss electric quantity prediction through an XGB OST algorithm;
1.4) carrying out self-electricity utilization prediction of the distributed clean energy based on a regression method.
4. The business cause-based economic activity prediction method for companies as claimed in claim 3, wherein in step 1.1), when predicting the new capacity of business expansion installation based on regression method, firstly, the lag relationship between the new capacity and the new capacity is grasped based on the historical business expansion installation new capacity and business expansion installation new electric quantity data to perform time difference correlation analysis; secondly, data preprocessing is carried out based on the analyzed hysteresis relation, regression analysis is carried out on the processed data to construct the quantitative relation between the future newly added electric quantity and the historical newly added capacity, and therefore prediction of the newly added capacity of the business expansion device is achieved.
5. The business incentive based economic activity prediction method for companies according to claim 3, wherein in the step 1.4), when the self-consumption electric quantity of the clean energy is predicted: firstly, user data of self-use of distributed clean energy sources are combed, the self-use data of the distributed clean energy sources in the users are collected and sorted, or the data are inversely calculated based on plant utilization rate and internet electricity quantity, and the number of the clients is counted; secondly, analyzing the relation between the self-electricity consumption of the distributed clean energy of the single user and electricity price, subsidy scale and seasonal weather, constructing a regression model, and predicting the self-electricity consumption scale of the distributed clean energy of the single user; thirdly, analyzing the correlation between the number of the self-use users of the distributed clean energy and the total number of the users, policies and electricity prices, constructing a regression prediction model, and predicting the number of the users of the distributed clean energy; and finally, calculating the self-consumption electric quantity of the clean energy in the prediction period based on the predicted self-consumption electric quantity of the single-user distributed clean energy and the number of the distributed clean energy users.
6. The business cause-based economic activity prediction method for the company, as claimed in claim 1, wherein in the step 2), the method for constructing the business cause-based power selling change prediction model comprises the following steps:
2.1) calculating the base value of the predicted period electricity sales amount based on the historical electricity sales amount and the average growth rate, namely
The predicted period electricity selling base value is the actual value of the last period electricity selling (1+ the average electricity selling increase rate of the last 3 years);
2.2) considering the forecast change value of the newly-increased capacity of the business expansion installation, the electric energy replacing electric quantity, the failure emergency repair loss electric quantity and the distributed clean energy self-use electric quantity, adjusting the basic value of the electricity selling quantity in the forecast period as the forecast electricity selling quantity, namely:
the predicted electricity sales amount is the predicted electricity sales amount basic value, the business expansion new capacity increasing electricity amount variation value, the electric energy replacing electricity amount, the fault first-aid repair loss electricity amount variation value and the distributed clean energy self-use electricity amount variation value.
7. The business incentive based economic activity forecasting method for companies according to claim 1, wherein the method for forecasting the profit margin in the step 3) comprises the following steps:
3.1) combing the income incentive value index parameters of the profit schedule, and determining the influence ways and the quantitative logics of the income on the income by the incentive value index parameters based on the service characteristics and the historical data characteristics;
3.2) combing the profit list cost factor value index parameters, and determining the influence ways and quantitative logics of the factor value index parameters on the cost based on the service characteristics and the historical data characteristics;
3.3) deducting cost items by using income items and calculating total profit; and deducting the income tax to calculate net profit.
8. The business incentive based economic activity prediction method for companies according to claim 1, wherein in the step 4), the method for predicting the balance sheet comprises the following steps:
4.1) combing asset dynamic factor value index parameters, and determining the influence ways and quantitative logics of the dynamic factor value index parameters on the assets based on the service characteristics and the historical data characteristics;
4.2) combing the liability and cause value index parameters, and determining the influence ways and the quantitative logic of each cause value index parameter on the assets based on the business characteristics and the historical data characteristics;
4.3) developing various equity predictions based on the owner equity change logic.
9. The business incentive based economic activity forecasting method for companies in the step 5), as claimed in claim 1, wherein the method for forecasting the cash flow table comprises the following steps:
5.1) carrying out prediction on the cash flow table of the operation activities;
the cash flow of the operation activity is net profit, financial expense, depreciation, amortization, operation capital change, non-cash investment income of long-term equity investment, long-term receivable reduction, other non-liquidity assets reduction, long-term receivable increase, long-term payable salary increase, special receivable increase, delayed income increase and other non-liquidity liability increase;
5.2) carrying out prediction on the cash flow of investment activities;
investment activity cash flow is fixed asset investment, fixed asset income disposal and fixed asset value reduction preparation increase, long-term equity investment and intangible asset investment;
5.3) carrying out prediction on cash flow of the financing activities;
the cash flow of the financing activity is equal to the increase of the interest bearing debt, the decrease of the interest bearing debt, the financial cost and the increase of the interest to be paid, the capital income of the paying country, the increase of the bond to be paid and the subsidy of the national fund;
5.4) capital gap measurement and calculation;
fund gap-lowest cash balance-initial fund balance-cash inflow in this period in addition to financing activities + cash outflow in this period.
10. The method for forecasting company economic activity based on business incentive according to claim 1, wherein in the step 6), the method for measuring and calculating the main performance indicators based on the forecasted profit sheets, cash flow sheets and asset liability sheets comprises the following steps:
6.1) determining main operation performance indexes to be measured and calculated, including gross profit, asset liability rate, EVA and net asset profitability;
6.2) measuring and calculating each determined performance index;
wherein the total profit amount is the total profit amount from the exterior to the interior;
the rate of assets liability is equal to liability/total amount of assets;
EVA (post-tax net operating profit-capital cost);
wherein, the net operating profit after tax is net profit + (interest expenditure + research and development fee) × (1-income tax); capital cost ═ (average owner equity + average interest bearing-average under construction) × capital cost rate, where capital cost rate ═ bond capital cost rate × interest bearing x (1-derived tax rate)/(average owner equity + average interest bearing) + equity capital cost rate × average owner equity/(average owner equity + average interest bearing).
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| CN114022193A (en) * | 2021-10-20 | 2022-02-08 | 国网福建省电力有限公司 | System establishment method of electric power enterprise standard cost dynamic library |
| CN115222540A (en) * | 2022-07-14 | 2022-10-21 | 北京融信数联科技有限公司 | Business circle consumption data analysis method, system and readable storage medium |
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| CN114022193A (en) * | 2021-10-20 | 2022-02-08 | 国网福建省电力有限公司 | System establishment method of electric power enterprise standard cost dynamic library |
| CN115222540A (en) * | 2022-07-14 | 2022-10-21 | 北京融信数联科技有限公司 | Business circle consumption data analysis method, system and readable storage medium |
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