Urban gas daily load prediction method based on GRA-ABC-BPNN

Abstract

The invention discloses an urban gas daily load prediction method based on a GRA-ABC-BP neural network. The method comprises the following steps: carrying out induction and quantitative analysis on factors influencing the urban gas daily load; a GRA (grey correlation analysis) method is adopted to eliminate influence factors with relatively small correlation, and variables input by the BP neural network are determined, so that a topological structure of the BP neural network is determined; when an ABC (artificial bee colony algorithm) is used for optimizing an initial weight and a threshold value of a BP neural network, the size of a population, the proportion of all bee species, the maximum number of iterations and restriction abandoning parameters are determined firstly; obtaining an optimal initial weight and an optimal threshold value through an ABC algorithm; gas daily load prediction is carried out by taking actual data of a certain city as a research example, a prediction result is obtained, and the accuracy and feasibility of the method are researched. According to the method disclosed by the invention, the gas load prediction problem is deeply researched from two aspects, namely screening research on a plurality of influence factors and optimization research on a BP neural network prediction model. And while the quality of the input neural network data is improved, the inherent defects of the BP neural network are optimized. Example research results show that the method is very high in prediction precision, the average absolute percentage error is as low as 0.5528%, and industrial requirements can be completely met.

Description

technical field [0001] The present invention relates to a kind of urban gas daily load prediction method based on GRA (Grey Relational Analysis)-ABC (Artificial Bee Colony Algorithm)-BP neural network model, so as to predict the gas load of a certain city in the next day, and then guide the city Gas transmission and distribution work. Background technique [0002] Gas daily load forecasting can provide basis for urban gas planning and design. Accurately predicting the daily load of urban gas is conducive to the efficient and reasonable allocation of urban gas resources, as well as the maintenance of pipeline networks, the reduction of energy consumption, and the reduction of operating costs. [0003] In recent years, with the development of intelligent algorithms, more and more combined models have been applied to the field of gas load forecasting. The Differential Evolution-Extreme Learning Machine (DE-ELM) model proposed by He Fangzhou et al. introduces the differential ...

AI Technical Summary

Problems solved by technology

[0004] By studying the research results of previous scholars and synthesizing the advantages and disadvantages of the research results, it can be concluded that the BP neural network model has a good effect on solving complex nonlinear problems, but it still has low learning efficiency, slow convergence speed, and random initialization. Problems such as weight and threshold and easy to fall into local minimum state

Therefore, it will also lead to problems such as poor prediction accuracy, unstable prediction results, and long learning time.

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