KPCA-LA-RBM-based transmission and transformation project cost prediction method

Abstract

The invention discloses KPCA-LA-RBM-based transmission and transformation project cost prediction method, and belongs to the electric digital data processing technology field. The method includes suchsteps as selecting and preprocessing data of each sample and obtaining key influencing factor set; the training set is composed of several groups of pre-processed sample data, and the training set isused to train KPCA-LA-RBM combination model; using the remaining data as the test set, according to the trained combination model, using the test set to predict the trained model, the final prediction results are obtained. The invention selects the first four principal components as input vectors of the combination model according to the principal component accumulative variance contribution rate, which improves the calculation efficiency of the model under the condition of guaranteeing the prediction accuracy. The combined model proposed by the invention can effectively reduce the error caused by a single model, improve the prediction accuracy, improve the generalization ability and robustness, and is suitable for the cost prediction of transmission and transformation projects.

Description

technical field [0001] The invention belongs to the technical field of electrical digital data processing, in particular to a KPCA-LA-RBM-based cost prediction method for power transmission and transformation projects. Background technique [0002] Electric power construction belongs to infrastructure construction. Compared with other consumer needs, the quality of infrastructure construction has a great impact on people's living standards and quality. It can be said that power construction is also a symbol of a city's level of development to a certain extent. In recent years, with the growth of my country's GDP, the power grid industry has developed rapidly, the scale of power transmission and transformation projects has continued to increase, and the investment in power grids has also become larger and larger. In order to reasonably control costs, optimize resource allocation and effectively adjust planning arrangements in the actual construction process of electric power,...

AI Technical Summary

Problems solved by technology

[0005] However, the BP neural network algorithm has a slow convergence speed and is easy to fall into a local optimal solution. In view of the shortcomings of the algorithm, many scholars use factor analysis, fuzzy algorithm, etc. to combine with the neural network to form a new combination algorithm, or use genetic algorithm, particle swarm optimization Algorithms, etc. to improve it

[0006] Although the BP neural network algorithm has been greatly improved in terms of prediction performance and can obtain more accurate prediction results, the BP neural network is not suitable for prediction of small sample data.

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