CN111047108B - Electric energy duty ratio prediction method in terminal energy consumption based on optimal combination model - Google Patents
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Abstract
The invention discloses a method for predicting the electric energy duty ratio in terminal energy consumption based on an optimal combination model, which comprises the steps of taking a trend extrapolation model as a single prediction model to obtain an energy-electric energy consumption predicted value; the gray prediction model is used as an energy-electric energy consumption prediction value calculated by a single prediction model; solving a prediction error of the trend extrapolation model and the gray prediction model; determining the weight of each single model in the combined model according to the inverse of the square sum of the model prediction errors, thereby determining the combined prediction model; determining optimal prediction points according to a model error minimum principle; based on the electric energy consumption and the energy consumption, the electric energy consumption and the energy consumption in the future year are respectively predicted by utilizing a combined prediction model, and the predicted value of the electric energy duty ratio PETEC in the terminal energy consumption is indirectly obtained through proportional operation. The method has a good prediction effect on the data sequence which presents a stable evolution rule along with the whole change of time, and can effectively predict the energy consumption trend.
Description
Technical Field
The invention belongs to the field of electric new energy research, and particularly relates to a method for predicting an electric energy duty ratio in terminal energy consumption based on an optimal combination model.
Background
In recent years, urban energy transformation promotes the improvement of regional energy consumption structures and the development of electric energy substitution processes, and the improvement of the electric energy consumption structures and the development of electric energy substitution processes in the future are further promoted, so that the remarkable increase of the electric energy consumption in the future regions and the continuous increase of the electric energy proportion in terminal energy consumption are also promoted; meanwhile, because the energy transformation policies and measures have stages, the electric energy ratio in regional terminal energy consumption increases to a certain extent and presents a random fluctuation time change trend, and a higher requirement is put forward for the electric power demand prediction work of the electric network company.
The modern prediction method mainly comprises a gray prediction method, a bionic algorithm, a wavelet analysis method, a combined prediction method, a fuzzy mathematic method, a support vector machine, a chaos prediction method and the like, wherein a gray GM (1, 1) prediction model of energy consumption is established according to energy consumption total amount data and coal, petroleum, natural gas and power consumption data in an energy consumption structure through research, radial basis function parameters are optimized through particle swarm optimization, and a resident living energy consumption prediction model based on the support vector machine technology is established, and the models have the following defects:
1. the prediction model is single. The single prediction model simplifies the prediction process, is difficult to comprehensively grasp the electrification level increase rule, limits the accuracy of energy and power consumption prediction, and better solves the problem due to the combination prediction method.
2. To take into account the electrical energy duty cycle PETEC in the terminal energy consumption. The electric energy ratio in terminal energy consumption is not only an important index for measuring the terminal energy consumption structure, but also an important mark for measuring the national or regional energy transformation and the development level, and the index is necessary for researching the energy and power consumption transformation trend under the urban energy transformation background.
In summary, it is necessary to consider the electrical energy duty ratio index in the terminal energy consumption, and perform appropriate prediction model combination at the same time, so as to predict the electrical energy duty ratio in the terminal energy consumption, and provide a construction basis for the development of the energy and power consumption conditions.
Disclosure of Invention
The invention aims to: in order to solve the problems, a construction basis is provided for the development of energy and power consumption conditions, and the invention provides a terminal energy consumption electric energy duty ratio prediction method based on an optimal combination model.
The technical scheme is as follows: in order to achieve the above purpose, the present invention adopts the following technical scheme:
a method for predicting the electric energy duty ratio in terminal energy consumption based on an optimal combination model comprises the following steps:
step S1, acquiring total energy consumption data and total energy consumption data of the past year n years before the year to be predicted, establishing a trend extrapolation prediction model, and obtaining an energy-energy consumption predicted value by taking the trend extrapolation model as a single prediction model;
s2, obtaining an energy-electric energy consumption predicted value by taking a gray predicted model as a single predicted model;
s3, respectively solving prediction errors of the trend extrapolation model and the gray prediction model;
s4, determining the weight of each single model in the combined model according to the inverse of the square sum of model prediction errors, so as to determine the combined prediction model;
s5, determining optimal prediction points according to a model error minimum principle;
and S6, respectively predicting the electric energy consumption and the energy consumption of the future year by using a combined prediction model based on the electric energy consumption and the energy consumption, and indirectly obtaining the predicted value of the electric energy duty ratio PETEC in the terminal energy consumption through proportional operation.
Further, in step S1, the trend extrapolation model is:
p n+1 ＝a 1 (n+1) 2 +a 2 (n+1)+a 3 (1)；
q n+1 ＝b 1 (n+1) 2 +b 2 (n+1)+b 3 (2)；
wherein a is 1 、a 2 、a 3 The values of the first three terms of the coefficient matrix a to be solved, p, for the total amount of electric energy consumption in the form of a trend extrapolation prediction model matrix n+1 B is the total amount of electric energy consumption in the n+1th year 1 、b 2 、b 3 The first three terms of the coefficient matrix b to be solved for the total energy consumption amount in the form of the trend extrapolation prediction model matrixValues of q n+1 The total amount of electric energy consumption in the n+1th year;
the solving method of a and b is as follows:
a＝(x T x) -1 x t P (3)；
b＝(x T x) -1 x T Q (4)；
wherein,
obtaining a predicted value pre of the electric energy ratio in terminal energy consumption in the n+1th year based on a trend extrapolation model according to the electric energy consumption total amount and the relation between the electric energy consumption total amount and the electric energy ratio in terminal energy consumption 1 The formula is as follows:
wherein R is L And R is eL R is the loss rate of energy and electric energy equ The coefficient is calculated for the standard coal of the electric energy.
Further, in step S2, the gray prediction model is used as the energy-energy consumption prediction value calculated by the single prediction model, the total energy consumption data P and the total energy consumption data Q of the last n years of the year to be predicted are accumulated respectively, new data sequences p_sum and q_sum are generated, and then a gray prediction model first-order differential equation is constructed for the new data sequences p_sum and q_sum respectively:
wherein c 1 、c 2 Solving coefficients of the total consumption of electric energy respectively, d 1 、d 2 Solving coefficients of the total consumption amount of the electric energy respectively;
the solution of the first order differential equation is exponential, i.e.:
wherein p_sum n+1 Q_sum, which is the sum of n+1 variables before the total amount of electric energy consumption n+1 N in formulas (8) and (9) is changed into n-1 to obtain the sum p_sum of n variables before the total energy consumption n And q_sum of n variables before total energy consumption n A predicted value p 'of the total amount of electric energy consumed in the n+1th year based on the gray prediction model' n+1 And a predictive value q 'of the total energy consumption' n+1 The calculation formulas are shown in formulas (10) - (11):
p′ n+1 ＝p_sum n+1 -p_sum n (10)；
q′ n+1 ＝q_sum n+1 -q_sum n (11)。
further, the prediction errors of the trend extrapolation model and the gray prediction model calculated in step S3:
wherein, is delta 11 ％、△ 12 % is the predicted value error, delta, of the total amount of electric energy consumed in the n+1th year based on the trend extrapolation model and on the gray prediction model, respectively 21 ％、△ 22 % is the forecast error of the total energy consumption of the n+1th year based on the trend extrapolation model and the gray forecast model, act 1 、act 2 The total consumption amount of electric energy and the total consumption amount of energy in the corresponding year are respectively.
Further, in step S4, the weight of each single model in the combined model is determined according to the inverse of the sum of squares of the model prediction errors, and the combined prediction model is determined as follows:
wherein, pre 1 Predictive value, pre, of total energy consumption determined for a combined model 1 Predictive value, ω, of total energy consumption determined for a combined model 1 、ω 2 Respectively representing the weight, omega of the predicted value of the total consumption amount of the electric energy in a combined model based on a trend extrapolation model and a gray prediction model 3 、ω 4 Respectively, the weight of the predicted value of the total consumption amount of the electric energy in a combined model based on a trend extrapolation model and a gray prediction model.
Further, in step S5, the optimal predicted point number J of the total power consumption and the total power consumption is determined according to the combined model error minimization principle 1 、J 2 The method comprises the steps of carrying out a first treatment on the surface of the The formula is as follows:
wherein, pre 1 (i) As the forecast value of the ith year of the total consumption of electric energy, pre 2 (i) And (3) taking the predicted value of the ith year of the total energy consumption as a predicted value, carrying out error square sum accumulation on all historical data capable of carrying out prediction error inspection under each predicted point k to obtain the optimal predicted point when the average value of each group of error square sums is minimum, wherein N is the total number of years covered by the historical data.
Further, step S6 uses the electric energy consumption E eT And energy consumption E T Based on the combined prediction model, the electric energy consumption pre of the future year is respectively predicted 1 And energy consumption pre 2 Indirectly obtaining the predicted value of the electric energy duty ratio PETEC in terminal energy consumption through proportional operation
Wherein R is equ The calculation coefficient R of standard coal for electric energy eL R is the loss rate of electric energy L PETEC is the electric energy ratio in the original terminal energy consumption, and is the energy loss rate E % and% e % is the relative prediction error of the electrical energy consumption and the energy consumption.
The beneficial effects are that: compared with the prior art, the method provided by the invention has the advantages that the PETEC combined prediction model is compatible with the trend extrapolation prediction model and the gray prediction model aiming at the change characteristics of the PETEC under the urban energy transformation background. The prediction error of the combined prediction model is reduced by selecting the number of the prediction points and the weights of different single models in the combination; the electric energy consumption total amount and the energy consumption total amount are predicted and calculated through the combined prediction model, and then the electric energy duty ratio in the terminal energy consumption is indirectly predicted through the mathematical relationship between the electric energy consumption total amount and the electric energy duty ratio in the terminal energy consumption, and the prediction error of the predicted value of the electric energy consumption total amount and the predicted value of the electric energy consumption total amount is considered in the indirect prediction process, so that the prediction model has more practical significance.
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Fig. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a method for predicting the electric energy duty ratio in terminal energy consumption based on an optimal combination model includes the following steps:
s1, acquiring total energy consumption data of the past year n years before the year to be predictedAnd energy consumption total amount data->Establishing a trend extrapolation prediction model, wherein the matrix form of the formula is as follows:
p n+1 ＝a 1 (n+1) 2 +a 2 (n+1)+a 3 (1)；
q n+1 ＝b 1 (n+1) 2 +b 2 (n+1)+b 3 (2)；
wherein a is 1 、a 2 、a 3 The values of the first three terms of the coefficient matrix a to be solved, p, for the total amount of electric energy consumption in the form of a trend extrapolation prediction model matrix n+1 B is the total amount of electric energy consumption in the n+1th year 1 、b 2 、b 3 Values q of first three terms of coefficient matrix b to be solved for energy consumption total amount in form of trend extrapolation prediction model matrix n+1 The total amount of electric energy consumption in the n+1th year.
The solving method of a and b is as follows:
a＝(x T x) -1 x t P (3)；
b＝(x T x) -1 x T Q (4)；
wherein,
a calculated by formulas (3) and (4) 1 、a 2 、a 3 、b 1 、b 2 、b 3 After the value of (2), substituting the value into the formulas (1) and (2) to respectively obtain the total electric energy consumption and the total energy consumption of the n+1th year based on the trend extrapolation model.
Step S2, accumulating the total energy consumption data P and the total energy consumption data Q of the last n years before the year to be predicted, generating new data sequences P_sum and Q_sum, and constructing a gray prediction model first-order differential equation for the new data sequences P_sum and Q_sum respectively:
wherein c 1 、c 2 Solving coefficients of the total consumption of electric energy respectively, d 1 、d 2 And the solution coefficients are respectively the total consumption amount of the electric energy.
The solution of the first order differential equation is exponential, i.e.:
wherein p_sum n+1 Q_sum, which is the sum of n+1 variables before the total amount of electric energy consumption n+1 N in formulas (7) and (8) is changed into n-1 to obtain the sum p_sum of n variables before the total energy consumption n And q_sum of n variables before total energy consumption n A predicted value p 'of the total amount of electric energy consumed in the n+1th year based on the gray prediction model' n+1 And a predictive value q 'of the total energy consumption' n+1 The calculation formulas are shown in formulas (9) - (10):
p′ n+1 ＝p_sum n+1 -p_sum n (9)；
q′ n+1 ＝q_sum n+1 -q_sum n (10)；
step S3, respectively solving the prediction errors of the trend extrapolation model and the gray prediction model, wherein the formula is as follows:
wherein, is delta 11 ％、△ 12 % is total amount of electric energy consumption in the n+1th year based on trend extrapolation model and gray prediction model, respectivelyPrediction value error, delta 21 ％、△ 22 % is the forecast error of the total energy consumption of the n+1th year based on the trend extrapolation model and the gray forecast model, act 1 、act 2 The total consumption amount of electric energy and the total consumption amount of energy in the corresponding year are respectively;
s4, determining the weight of each single model in the combined model according to the inverse of the square sum of model prediction errors, and determining the combined model, wherein the formula is as follows:
wherein, pre 1 To the predicted value of the total electric energy consumption based on the combined model, pre 1 Omega is a predicted value of total energy consumption based on a combined model 1 、ω 2 Respectively representing the weight, omega of the predicted value of the total consumption amount of the electric energy in a combined model based on a trend extrapolation model and a gray prediction model 3 、ω 4 Respectively representing the weights of the predicted value of the total consumption amount of the electric energy in a combined model based on a trend extrapolation model and a gray prediction model, wherein the calculation method is as follows:
wherein D is 1 、D 2 The total error square sum of the total electric energy consumption in the trend extrapolation model and the gray prediction model is calculated as follows:
wherein act is 1 (i) Act, which is the actual value of the total amount of electric energy consumed in the ith year 2 (i) For the actual value of the total energy consumption in the ith year, p i 、q i The energy-electric energy consumption predicted value of the ith year is respectively a trend extrapolation model and a gray prediction model, and p is the prediction accuracy testYears of (2);
s5, respectively determining the total electric energy consumption and the optimal predicted point J of the total energy consumption according to the principle of minimum combined model error 1 、J 2 . The formula is as follows:
wherein, pre 1 (i) Predictive value, pre, for the ith year of the total amount of electric energy consumption based on the combined model 2 (i) Performing error square sum accumulation on all historical data capable of performing prediction error check under each predicted point k to obtain the predicted value of the ith year of the combined model, wherein the corresponding predicted point is the optimal predicted point when the average value of each group of error square sums is minimum, and N is the total number of years covered by the historical data;
step S6, using the electric energy consumption E eT And energy consumption E T Based on the combined prediction model, the electric energy consumption pre of the future year is respectively predicted 1 And energy consumption pre 2 Indirectly obtaining the predicted value of the electric energy duty ratio PETEC in terminal energy consumption through proportional operation
Wherein R is equ The calculation coefficient R of standard coal for electric energy eL R is the loss rate of electric energy L PETEC is the electric energy ratio in the original terminal energy consumption, and is the energy loss rate E % and% e % is the relative prediction error of the electric energy consumption and the energy consumption, and the calculation method is as follows:
the above detailed description of the specific embodiments further describes the present disclosure in detail, and some parameters and functions are instantiated, so that the present disclosure may be replaced by equivalent ones in practical application, and suitable parameters may be selected according to circumstances.
According to the method for predicting the electric energy duty ratio in the terminal energy consumption based on the optimal combination model, the trend extrapolation model and the gray prediction model are selected as single prediction models according to sequence characteristics and prediction requirements respectively based on the total annual electric energy consumption data and the total energy consumption data, weights are obtained according to single model errors, so that the combination prediction model is determined, the optimal prediction point number is determined according to the principle of minimum model errors, the electric energy consumption and the energy consumption are used as the basis, the electric energy consumption and the energy consumption in the future year are predicted respectively by using the combination prediction model, PETEC prediction values are obtained indirectly through proportion operation, and the terminal electric energy consumption proportion prediction recommended values are determined.
Claims (1)
1. The method for predicting the electric energy duty ratio in terminal energy consumption based on the optimal combination model is characterized by comprising the following steps:
step S1, acquiring total energy consumption data and total energy consumption data of the past year n years before the year to be predicted, establishing a trend extrapolation prediction model, and obtaining an energy-energy consumption predicted value by taking the trend extrapolation model as a single prediction model;
the trend extrapolation model is:
p n+1 ＝a 1 (n+1) 2 +a 2 (n+1)+a 3 (1)；
q n+1 ＝b 1 (n+1) 2 +b 2 (n+1)+b 3 (2)；
wherein a is 1 、a 2 、a 3 Values p of the first three terms of the coefficient matrix a to be solved, respectively of the total amount of electric energy consumption in the form of a trend extrapolation prediction model matrix n+1 B is the total amount of electric energy consumption in the n+1th year 1 、b 2 、b 3 To-be-solved system of total energy consumption in form of trend extrapolation prediction model matrixThe values of the first three terms, q, of the number matrix b n+1 The total energy consumption amount is n+1th year;
the solving method of a and b is as follows:
a＝(x T x) -1 x t P (3)；
b＝(x T x) -1 x T Q (4)；
wherein,
obtaining a predicted value pre of the electric energy ratio in terminal energy consumption in the n+1th year based on a trend extrapolation model according to the electric energy consumption total amount and the relation between the electric energy consumption total amount and the electric energy ratio in terminal energy consumption 1 The formula is as follows:
wherein R is L And R is eL R is the loss rate of energy and electric energy equ Calculating coefficients for standard coal of electric energy;
s2, obtaining an energy-electric energy consumption predicted value by taking a gray predicted model as a single predicted model;
accumulating the total energy consumption data P and the total energy consumption data Q of the last n years of the year to be predicted respectively, generating new data sequences P_sum and Q_sum, and then constructing a gray prediction model first-order differential equation for the new data sequences P_sum and Q_sum respectively:
wherein c 1 、c 2 Respectively, the total consumption of electric energySolution coefficient d 1 、d 2 Solving coefficients of the total energy consumption amount respectively;
the solution of the first order differential equation is exponential, i.e.:
wherein p_sum n+1 Q_sum, which is the sum of n+1 variables before the total amount of electric energy consumption n+1 N in formulas (8) and (9) is changed into n-1 to obtain the sum p_sum of n variables before the total energy consumption n And q_sum of n variables before total energy consumption n A predicted value p 'of the total amount of electric energy consumed in the n+1th year based on the gray prediction model' n+1 And a predictive value q 'of the total energy consumption' n+1 The calculation formulas are shown in formulas (10) - (11):
p′ n+1 ＝p_sum n+1 -p_sum n (10)；
q′ n+1 ＝q_sum n+1 -q_sum n (11)；
step S3, respectively solving the prediction errors of the trend extrapolation model and the gray prediction model as follows:
wherein, is delta 11 ％、△ 12 % is the predicted value error, delta, of the total amount of electric energy consumed in the n+1th year based on the trend extrapolation model and on the gray prediction model, respectively 21 ％、△ 22 % is the forecast error of the total energy consumption of the n+1th year based on the trend extrapolation model and the gray forecast model, act 1 、act 2 Electric energy elimination for corresponding yearsTotal amount of fees and total amount of energy consumption;
s4, determining the weight of each single model in the combined model according to the inverse of the square sum of model prediction errors, so as to determine that the combined prediction model is:
wherein, pre 1 Predictive value, pre, of total energy consumption determined for a combined model 2 Predictive value, ω, of total energy consumption determined for a combined model 1 、ω 2 Respectively representing the weight, omega of the predicted value of the total consumption amount of the electric energy in a combined model based on a trend extrapolation model and a gray prediction model 3 、ω 4 Respectively representing the weights of the energy consumption total prediction values in a combined model based on a trend extrapolation model and a gray prediction model;
s5, determining optimal prediction points according to a model error minimum principle;
optimal predicted point number J of total electric energy consumption and total energy consumption 1 、J 2 The method comprises the steps of carrying out a first treatment on the surface of the The formula is as follows:
wherein, pre 1 (i) As the forecast value of the ith year of the total consumption of electric energy, pre 2 (i) Performing error square sum accumulation on all historical data capable of performing predictive error test under each predicted point k to obtain an i-th predicted value of the total energy consumption, wherein the corresponding predicted point is the optimal predicted point when the average value of each group of error square sums is minimum, and N is the total number of years covered by the historical data;
step S6, using the electric energy consumption E eT And energy consumption E T Based on the combined prediction model, the electric energy consumption pre of the future year is respectively predicted 1 And energy consumption pre 2 Indirectly obtaining the terminal energy through proportional operationPredicted value of PETEC (PETEC-based electric energy transfer electric power) ratio in source consumption
Wherein R is equ The calculation coefficient R of standard coal for electric energy eL R is the loss rate of electric energy L PETEC is the electric energy ratio in the original terminal energy consumption, and is the energy loss rate E % and% e % is the relative prediction error of the electrical energy consumption and the energy consumption.
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