CN113344292A - Method for predicting construction engineering investment of pumped storage power station by using neural network algorithm - Google Patents
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Abstract
The invention relates to a method for predicting construction engineering investment of a pumped storage power station by utilizing a neural network algorithm. The invention is suitable for the technical field of electric power engineering. The invention aims to provide a method for predicting the construction engineering investment of a pumped storage power station by utilizing a neural network algorithm, so that the method is quick and accurate, the operability is strong, and the manpower investment of a large number of professional designers and cost personnel at the early stage is reduced. The invention establishes the investment prediction model after full iterative training, can more quickly predict the investment of each part of the building engineering of the pumped storage power station according to the price characteristic factor and the design characteristic factor of the pumped storage power station, can control the result error of each part of the investment estimation within 10 percent, can provide more accurate estimation results in the investment estimation work of the early decision stage of the pumped storage power station project, simultaneously saves the manpower input of a large number of designers and construction cost personnel, and improves the efficiency of the investment estimation.
Description
Technical Field
The invention relates to a method for predicting construction engineering investment of a pumped storage power station by utilizing a neural network algorithm. Is applicable to the technical field of electric power engineering.
Background
At present, in the early decision-making stage of a pumped storage power station, a quota analysis method is mainly adopted for estimating the project investment, basic data needs to be collected, a design scheme is determined, the project amount is calculated according to the design scheme, the corresponding unit price is compiled for each project amount, and the investment is estimated after the unit price is made. The method needs a large amount of professional designers and cost personnel investment, and has the main problems of more input human resources, long duration and lower efficiency.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the existing problems, the method for predicting the construction engineering investment of the pumped storage power station by using the neural network algorithm is provided, so that the method is quick and accurate, the operability is strong, and the manpower investment of a large number of professional designers and cost personnel at the early stage is reduced.
The technical scheme adopted by the invention is as follows: a method for predicting the construction engineering investment of a pumped storage power station by utilizing a neural network algorithm is characterized by comprising the following steps of:
determining characteristic factors which influence each part of engineering of the pumped storage power station building engineering and are related to the engineering investment;
collecting and processing characteristic factor data of a pumped storage power station sample, wherein the characteristic factor data is divided into quantitative parameters and qualitative parameters, and the quantitative parameters are subjected to data normalization processing in a linear transformation mode; the quantitative parameters are firstly subjected to quantization processing, the quantization principle is that the more complex the construction process is, the greater the implementation difficulty is, the greater the engineering cost is, the larger the parameters are, and then the quantized parameters are subjected to normalization processing;
inputting the processed characteristic factor data of each part of engineering in a sample and the investment of each part of engineering into an investment prediction model which is established on the basis of a neural network algorithm and corresponds to each part of the engineering for training, and enabling the prediction model to tend to be accurate and stable after full iterative training;
and inputting characteristic factor data of all parts of the pumped storage power station building project to be predicted into the trained investment prediction model, and outputting the engineering investment of all parts of the pumped storage power station building project to be predicted and the total investment of the building project after addition.
The construction engineering of the pumped storage power station comprises water retaining engineering, water drainage engineering, water delivery engineering, power generation engineering, traffic engineering and other construction engineering.
The characteristic factors include price characteristic factors and design characteristic factors.
The price characteristic factor input parameters comprise average labor cost budget unit price, cement budget unit price, steel bar budget unit price and diesel oil budget unit price.
The design characteristic factor input parameters are as follows:
the construction method of the investment prediction model comprises the following steps:
neural network creation function: newff, which is used to create the BP network, the call format is as follows:
net＝newff(P,T,S,TF,BTF)
wherein, P: inputting a parameter matrix; t: a target parameter matrix; s: the number of hidden layers; TF: the transfer function of the related layer is that the default hidden layer is a tansig function, and the output layer is a purelin function; tansig: tangent S-shaped transfer function; purelin: a linear transfer function; BTF: the BP neural network learns a training function, and the default value is a rainlm function;
in the formula: s (i) is the number of neurons in the hidden layer, n is the number of input nodes, m is the number of output nodes, and a is a constant between 1 and 10.
The invention has the beneficial effects that: the invention establishes the investment prediction model after full iterative training, can more quickly predict the investment of each part of the building engineering of the pumped storage power station according to the price characteristic factor and the design characteristic factor of the pumped storage power station, can control the result error of each part of the investment estimation within 10 percent, can provide more accurate estimation results in the investment estimation work of the early decision stage of the pumped storage power station project, simultaneously saves the manpower input of a large number of designers and construction cost personnel, and improves the efficiency of the investment estimation.
Drawings
FIG. 1 is a block flow diagram of an embodiment.
Detailed Description
The embodiment is a method for predicting the construction engineering investment of a pumped storage power station by utilizing a neural network algorithm, which specifically comprises the following steps:
and S1, determining characteristic factors influencing various projects of the pumped storage power station building project and related to project investment.
According to the characteristics of the pumped storage power station construction engineering, the pumped storage power station construction engineering is divided into a plurality of parts, including a water retaining engineering (an upper warehouse engineering and a lower warehouse engineering), a water drainage engineering, a water delivery engineering, a power generation engineering, a traffic engineering and other construction engineering. The most relevant characteristic factors of each part of selection and engineering investment comprise two categories: price characterizing factors and design characterizing factors.
The price characteristic factor input parameters in this example include: the average labor cost budget unit price, the cement budget unit price, the steel bar budget unit price and the diesel oil budget unit price; design feature factor input parameters are as follows:
and S2, collecting and processing characteristic factor data of the pumped storage power station sample, wherein the characteristic factor data are divided into quantitative parameters and qualitative parameters for processing respectively.
Quantitative class parameters: the data normalization processing is carried out by adopting a linear transformation mode, wherein the linear transformation formula is as follows:
can be realized by Matlab codes: [ Pn, PS ] ═ mapminmax (P,0, 1).
Qualitative class parameters: the quantitative processing method is adopted, the corresponding quantitative numerical value of the high construction cost is large, and the main principle of the quantification is that the more complex the construction process is, the larger the implementation difficulty is, the larger the construction cost is, and the larger the parameter is. The quantification is followed by the normalization described above, as exemplified in the following table:
dam shape quantitative processing data
Design seismic strength quantitative processing data
S3, inputting the processed characteristic factor data of each part of engineering into an investment prediction model established by the corresponding engineering part based on a neural network algorithm, and enabling the prediction model to tend to be accurate and stable through full iterative training;
and S4, inputting the characteristic factors of each part of the pumped storage power station building project to be predicted into the trained investment prediction model, and outputting the project investment of each part of the pumped storage power station building project to be predicted and the total construction project investment after addition.
The construction method of the investment prediction model in the embodiment comprises the following steps:
1) neural network creation function: newff, which is used to create the BP network, the call format is as follows:
net＝newff(P,T,S,TF,BTF)
wherein, P: inputting a parameter matrix; t: a target parameter matrix; s: the number of hidden layers; TF: the transfer function of the related layer is that the default hidden layer is a tansig function, and the output layer is a purelin function; tansig: tangent S-shaped transfer function; purelin: a linear transfer function; BTF: the BP neural network learns a training function, and the default value is a rainlm function;
according to the formula:
in the formula: (i) the number of neurons in the hidden layer, n is the number of input nodes, m is the number of output nodes, and a is a constant between 1 and 10;
the number of nodes of the input layer in this embodiment is: the method comprises the following steps of: 13; water discharge engineering: 10; thirdly, water delivery engineering: 17; fourthly, power generation and transformation engineering: 14; permanent traffic engineering: 11; number of output layer nodes: the output is the engineering investment, and the number of the nodes of the output layer is 1.
By substituting the formula, the value range of S (i) is 5 to 14. Tests show that the number of the hidden layers is 8-12, so that the results are not very different and better.
2) Neural network simulation function: sim, after the network training is finished, the trained network is called by the function, and the calling format is as follows:
y＝sim(net,P)
wherein, net: a trained neural network; p: an input of the network; y: the actual output of the network pair P;
3) after several trial calculations and adjustments, the following parameters were determined for the neural network model configuration:
neural network model configuration parameters
4) Error control
The error on the training set and the test set is controlled within 10%.
Claims (6)
1. A method for predicting the construction engineering investment of a pumped storage power station by utilizing a neural network algorithm is characterized by comprising the following steps of:
determining characteristic factors which influence each part of engineering of the pumped storage power station building engineering and are related to the engineering investment;
collecting and processing characteristic factor data of a pumped storage power station sample, wherein the characteristic factor data is divided into quantitative parameters and qualitative parameters, and the quantitative parameters are subjected to data normalization processing in a linear transformation mode; the quantitative parameters are firstly subjected to quantitative processing, the quantitative principle is that the more complex the construction process is, the greater the implementation difficulty is and the greater the engineering cost is, the larger the parameters are, and the data normalization processing is carried out in a linear transformation mode after the quantitative processing;
inputting the processed characteristic factor data of each part of engineering in a sample and the investment of each part of engineering into an investment prediction model which is established on the basis of a neural network algorithm and corresponds to each part of the engineering for training, and enabling the prediction model to tend to be accurate and stable after full iterative training;
and inputting characteristic factor data of all parts of the pumped storage power station building project to be predicted into the trained investment prediction model, and outputting the engineering investment of all parts of the pumped storage power station building project to be predicted and the total investment of the building project after addition.
2. The method of predicting pumped storage power plant construction investment utilizing neural network algorithms of claim 1, wherein: the construction engineering of the pumped storage power station comprises water retaining engineering, water drainage engineering, water delivery engineering, power generation engineering, traffic engineering and other construction engineering.
3. The method for predicting the construction engineering investment of pumped storage power stations using neural network algorithms according to claim 1 or 2, characterized in that: the characteristic factors include price characteristic factors and design characteristic factors.
4. The method of predicting pumped storage power plant construction investment utilizing neural network algorithms of claim 3, wherein: the price characteristic factor input parameters comprise average labor cost budget unit price, cement budget unit price, steel bar budget unit price and diesel oil budget unit price.
5. The method of predicting pumped storage power plant construction investment utilizing neural network algorithms of claim 3, wherein said design feature factor input parameters are as follows:
6. the method for predicting pumped storage power plant construction engineering investment using neural network algorithm of claim 1, wherein the method of constructing the investment prediction model comprises:
neural network creation function: newff, which is used to create the BP network, the call format is as follows:
net＝newff(P,T,S,TF,BTF)
wherein, P: inputting a parameter matrix; t: a target parameter matrix; s: the number of hidden layers; TF: the transfer function of the related layer is that the default hidden layer is a tansig function, and the output layer is a purelin function; tansig: tangent S-shaped transfer function; purelin: a linear transfer function; BTF: the BP neural network learns a training function, and the default value is a rainlm function;
in the formula: s (i) is the number of neurons in the hidden layer, n is the number of input nodes, m is the number of output nodes, and a is a constant between 1 and 10.
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