CN105354371A - GA-WNN based power transmission and transformation project construction cost prediction method - Google Patents
GA-WNN based power transmission and transformation project construction cost prediction method Download PDFInfo
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Abstract
The present invention discloses a GA-WNN based power transmission and transformation project construction cost prediction method. The method comprises the steps of: collecting and analyzing power transmission and transformation project construction cost data, and by means of parameter testing and factor analysis, determining a power transmission and transformation project construction cost prediction index, and establishing a power transmission and transformation project construction cost index system; according to a grey relation analysis method, calculating a weight of the power transmission and transformation project construction cost prediction index, and merging the power transmission and transformation project construction cost prediction index into a first-grade index; performing optimization on a weight of a wavelet Neural Network by using a genetic algorithm, and by combining Matlab and a genetic algorithm tool box, constructing a GA-WNN based power transmission and transformation project construction cost prediction model; and performing simulation prediction on a power transmission and transformation project construction cost after training and testing. Provided is a power transmission and transformation project construction cost prediction method that has high application value, is simple and easy to operate, and is efficient and accurate, so that the power transmission and transformation project construction cost of a region can be accurately and reliably predicted, and reference and guidance functions can be provided for investment of the national power grid department.
Description
Technical field
The present invention relates to a kind of construction costs Forecasting Methodology, particularly relate to a kind of project of transmitting and converting electricity cost forecasting method based on GA-WNN, belong to field of power.
Background technology
Power industry is the important energy source industrial sector of Chinese national economy, for Chinese society economic development provides energy safeguard.The efficient construction of project of transmitting and converting electricity and stable operation, not only directly affect the development of macroeconomy and power industry self, also produces huge affecting mechanism to the other field of relevant industries.
The fast development of China's economy, causes need for electricity rapidly to increase, and only has the construction of Efforts To Develop project of transmitting and converting electricity, could alleviate the situation of power supply shortage.But with the development of advancing by leaps and bounds of building, also highlighted in current project of transmitting and converting electricity construction and there are some problems, namely investment is large, benefit is low, Cost Management exists the problems such as leak.Therefore, project of transmitting and converting electricity cost effectively being predicted, having important practical significance for instructing project of transmitting and converting electricity construction.
Summary of the invention
Fundamental purpose of the present invention is, overcome deficiency of the prior art, a kind of project of transmitting and converting electricity cost forecasting method based on genetic algorithm-wavelet neural network (GA-WNN) is provided, not only simple, accurately and reliably can dope the project of transmitting and converting electricity cost in somewhere, and have wide range of applications, reference value is high, practical, thus realize effectively prediction, for project of transmitting and converting electricity investment construction provides guidance.
In order to achieve the above object, the technical solution adopted in the present invention is:
Based on a project of transmitting and converting electricity cost forecasting method of GA-WNN, comprise the following steps:
1) collect the project of transmitting and converting electricity cost data of 3-5 in geographic coverage, set up project of transmitting and converting electricity cost sample database;
2) use statistical analysis software to carry out characteristic index analysis to project of transmitting and converting electricity cost data, by parametric test and factorial analysis, determine project of transmitting and converting electricity cost forecasting index, set up project of transmitting and converting electricity cost forecasting index system;
3) according to Grey Incidence Analysis, calculate the weight of project of transmitting and converting electricity cost forecasting index, project of transmitting and converting electricity cost forecasting index is integrated into first class index;
4) using first class index good for merger as input, project of transmitting and converting electricity prediction cost is as output, utilize the weights of genetic algorithm to wavelet neural network to be optimized, in conjunction with Matlab and GAs Toolbox, build the project of transmitting and converting electricity cost forecasting model based on GA-WNN;
5) based on GA-WNN project of transmitting and converting electricity cost forecasting model by carry out after training and testing project of transmitting and converting electricity prediction cost simulation and prediction.
The present invention is set to further: described step 1) also comprise renewal to project of transmitting and converting electricity cost sample database; By constantly collecting new project of transmitting and converting electricity cost data, and being increased in project of transmitting and converting electricity cost sample database, being completed the renewal of the sample size of project of transmitting and converting electricity cost sample database.
The present invention is set to further: described characteristic index analysis adopts regression analysis, principal component analysis (PCA) and variance analysis method.
The present invention is set to further: described step 3) in project of transmitting and converting electricity cost forecasting index is integrated into the merging method that first class index takes and is, according to the attribute assignment of project of transmitting and converting electricity cost forecasting index and the multiplied by weight summation thereof that belong to three major types engineering, obtain first class index value.
The present invention is set to further: described step 5) in training and testing be specially, project of transmitting and converting electricity cost sample database is divided into training set and test set, input amendment is trained as forecast model by training set, when meeting accuracy requirement, determine structure and the weighted value of forecast model, the training effect of forecast model is detected again, until precision reaches target setting by test set.
The present invention is set to further: the anticipation error of described target setting is ε=0.0001.
Compared with prior art, the beneficial effect that the present invention has is:
There is provided a kind of and there is higher using value, project of transmitting and converting electricity cost forecasting method that is simple, efficiently and accurately, accurately and reliably can dope the project of transmitting and converting electricity cost in somewhere, thus provide reference for national grid department invests, directive function is played to the project of transmitting and converting electricity construction in somewhere; And have wide range of applications, there are reference and using value in the periphery a lot of area of project of transmitting and converting electricity cost to prediction area of prediction gained; Also according to prediction cost situation of change, the factor affecting power transmission and transformation cost in certain region can be monitored, the impact that analysis policy, price etc. cause project of transmitting and converting electricity cost in addition.
Foregoing is only the general introduction of technical solution of the present invention, and in order to clearer understanding technological means of the present invention, below in conjunction with accompanying drawing, the invention will be further described.
Accompanying drawing explanation
Fig. 1 is the error change schematic diagram of forecast model training in the present invention.
Embodiment
Below in conjunction with Figure of description, the present invention is further illustrated.
The invention provides a kind of project of transmitting and converting electricity cost forecasting method based on GA-WNN, comprise the following steps:
1) collecting the project of transmitting and converting electricity cost data of 3-5 in geographic coverage, as collected from Jiangsu Province, setting up project of transmitting and converting electricity cost sample database; The project of transmitting and converting electricity cost sample database set up can be constantly updated, specifically by constantly collecting new project of transmitting and converting electricity cost data, and being increased in project of transmitting and converting electricity cost sample database, completing sample size dilatation and renewal.
2) statistical analysis software as SPSS and so on is used to carry out characteristic index analysis to project of transmitting and converting electricity cost data, regression analysis, principal component analysis (PCA) and variance analysis method can be adopted, by parametric test and factorial analysis, determine project of transmitting and converting electricity cost forecasting index, set up project of transmitting and converting electricity cost forecasting index system as shown in table 1.
Table 1
3) according to Grey Incidence Analysis, calculate the weight of project of transmitting and converting electricity cost forecasting index, project of transmitting and converting electricity cost forecasting index is integrated into first class index; The merging method taked is, according to the attribute assignment of project of transmitting and converting electricity cost forecasting index and the multiplied by weight summation thereof that belong to three major types engineering, obtains first class index value.
4) using first class index good for merger as input, project of transmitting and converting electricity prediction cost is as output, the weights of genetic algorithm to wavelet neural network are utilized to be optimized, input, the output vector of wavelet neural network are [0,1] number between, is normalized the engineering characteristics property value of collected sample
in conjunction with Matlab and GAs Toolbox, build the project of transmitting and converting electricity cost forecasting model based on GA-WNN.
5) based on GA-WNN project of transmitting and converting electricity cost forecasting model by carry out after training and testing project of transmitting and converting electricity prediction cost simulation and prediction.Be specially, project of transmitting and converting electricity cost sample database is divided into training set and test set, input amendment is trained as forecast model by training set, in error change situation as shown in Figure 1, when meeting accuracy requirement, determine structure and the weighted value of forecast model, then detect the training effect of forecast model by test set, until carry out simulation and prediction when precision reaches target setting, wherein the anticipation error of target setting is ε=0.0001.
More than show and describe ultimate principle of the present invention, principal character and advantage.The technician of the industry should understand; the present invention is not restricted to the described embodiments; what describe in above-described embodiment and instructions just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.Application claims protection domain is defined by appending claims and equivalent thereof.
Claims (6)
1., based on a project of transmitting and converting electricity cost forecasting method of GA-WNN, it is characterized in that, comprise the following steps:
1) collect the project of transmitting and converting electricity cost data of 3-5 in geographic coverage, set up project of transmitting and converting electricity cost sample database;
2) use statistical analysis software to carry out characteristic index analysis to project of transmitting and converting electricity cost data, by parametric test and factorial analysis, determine project of transmitting and converting electricity cost forecasting index, set up project of transmitting and converting electricity cost forecasting index system;
3) according to Grey Incidence Analysis, calculate the weight of project of transmitting and converting electricity cost forecasting index, project of transmitting and converting electricity cost forecasting index is integrated into first class index;
4) using first class index good for merger as input, project of transmitting and converting electricity prediction cost is as output, utilize the weights of genetic algorithm to wavelet neural network to be optimized, in conjunction with Matlab and GAs Toolbox, build the project of transmitting and converting electricity cost forecasting model based on GA-WNN;
5) based on GA-WNN project of transmitting and converting electricity cost forecasting model by carry out after training and testing project of transmitting and converting electricity prediction cost simulation and prediction.
2. the project of transmitting and converting electricity cost forecasting method based on GA-WNN according to claim 1, is characterized in that: described step 1) also comprise renewal to project of transmitting and converting electricity cost sample database; By constantly collecting new project of transmitting and converting electricity cost data, and being increased in project of transmitting and converting electricity cost sample database, being completed the renewal of the sample size of project of transmitting and converting electricity cost sample database.
3. the project of transmitting and converting electricity cost forecasting method based on GA-WNN according to claim 1, is characterized in that: described characteristic index analysis adopts regression analysis, principal component analysis (PCA) and variance analysis method.
4. the project of transmitting and converting electricity cost forecasting method based on GA-WNN according to claim 1, it is characterized in that: described step 3) in project of transmitting and converting electricity cost forecasting index is integrated into the merging method that first class index takes and is, according to the attribute assignment of project of transmitting and converting electricity cost forecasting index and the multiplied by weight summation thereof that belong to three major types engineering, obtain first class index value.
5. the project of transmitting and converting electricity cost forecasting method based on GA-WNN according to claim 1, it is characterized in that: described step 5) in training and testing be specially, project of transmitting and converting electricity cost sample database is divided into training set and test set, input amendment is trained as forecast model by training set, when meeting accuracy requirement, determine structure and the weighted value of forecast model, then detect the training effect of forecast model by test set, until precision reaches target setting.
6. the project of transmitting and converting electricity cost forecasting method based on GA-WNN according to claim 5, is characterized in that: the anticipation error of described target setting is ε=0.0001.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107563612A (en) * | 2017-08-16 | 2018-01-09 | 国网天津市电力公司电力科学研究院 | A kind of analysis method of power station titanium alloy tube sheet cost |
| CN109214503A (en) * | 2018-08-01 | 2019-01-15 | 华北电力大学 | Project of transmitting and converting electricity cost forecasting method based on KPCA-LA-RBM |
| CN109818775A (en) * | 2018-12-14 | 2019-05-28 | 南昌大学 | Short-term network method for predicting based on adaptive differential evolution algorithm Optimization of Wavelet neural network |
| CN111007918A (en) * | 2019-12-11 | 2020-04-14 | 国网辽宁省电力有限公司经济技术研究院 | Electric power engineering cost prediction model based on artificial neural network |
| CN112348656A (en) * | 2020-09-29 | 2021-02-09 | 百维金科(上海)信息科技有限公司 | BA-WNN-based personal loan credit scoring method |
| CN113128125A (en) * | 2021-04-23 | 2021-07-16 | 广东电网有限责任公司 | Method and device for predicting quantity of power transmission and transformation engineering material |
| CN113158292A (en) * | 2021-03-02 | 2021-07-23 | 广联达科技股份有限公司 | Component matching method, engineering quantity calculation method, device and electronic equipment |
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| CN103700030A (en) * | 2013-12-16 | 2014-04-02 | 国家电网公司 | Grey rough set-based power grid construction project post-evaluation index weight assignment method |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107563612A (en) * | 2017-08-16 | 2018-01-09 | 国网天津市电力公司电力科学研究院 | A kind of analysis method of power station titanium alloy tube sheet cost |
| CN109214503A (en) * | 2018-08-01 | 2019-01-15 | 华北电力大学 | Project of transmitting and converting electricity cost forecasting method based on KPCA-LA-RBM |
| CN109214503B (en) * | 2018-08-01 | 2021-09-10 | 华北电力大学 | Power transmission and transformation project cost prediction method based on KPCA-LA-RBM |
| CN109818775A (en) * | 2018-12-14 | 2019-05-28 | 南昌大学 | Short-term network method for predicting based on adaptive differential evolution algorithm Optimization of Wavelet neural network |
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| CN112348656A (en) * | 2020-09-29 | 2021-02-09 | 百维金科(上海)信息科技有限公司 | BA-WNN-based personal loan credit scoring method |
| CN113158292A (en) * | 2021-03-02 | 2021-07-23 | 广联达科技股份有限公司 | Component matching method, engineering quantity calculation method, device and electronic equipment |
| CN113158292B (en) * | 2021-03-02 | 2024-02-13 | 广联达科技股份有限公司 | Component matching method, engineering amount calculating device and electronic equipment |
| CN113128125A (en) * | 2021-04-23 | 2021-07-16 | 广东电网有限责任公司 | Method and device for predicting quantity of power transmission and transformation engineering material |
| CN113128125B (en) * | 2021-04-23 | 2023-04-07 | 广东电网有限责任公司 | Method and device for predicting quantity of power transmission and transformation engineering material |
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