Short-term Natural Gas Load Forecasting Based On Wavelet Neural Network Optimized By Genetic Algorithm

With the improvement of people’s quality of life and the increasing awareness of environmental protection,the natural gas,as a high-quality,efficient and environmental friendly fossil energy,has gradually entered the public’s vision and has been vigorously promoted and used.In recent years,with the continuous progress of smart city construction,the construction of smart natural gas pipeline networks is also accelerating.In order to enable the natural gas supply system to operate efficiently and perform scientific management,it is required that we master the characteristics and change laws of natural gas loads and make accurate and real-time intelligent predictions.Accurate natural gas load prediction is of great significance for improving the social and economic benefits of gas companies and maintaining the safe and stable operation of natural gas systems.The traditional natural gas prediction model has low prediction accuracy and low generalization of the model.Therefore,in response to these problems,this paper studies the processing method of natural gas load data and load prediction model.Based on the existing research,a new load prediction method is proposed.The main research methods of this article are as follows:(1)An improved data denoising method is proposed to preprocess the mining data.The collected natural gas load data is often mixed with different types of noise data.The addition of these data will greatly affect the establishment of the model,so such data needs to be eliminated before modeling.The traditional wavelet noise reduction method has the problem of discontinuous threshold function which may produce signal distortion and deviations between wavelet coefficients.In order to improve the effect of noise reduction,an improved threshold function is proposed for noise reduction.It has good continuity and is convenient for mathematical operations.Also can effectively overcome the shortcomings of the traditional threshold function in the denoising process and achieve better results.(2)Research on natural gas load forecasting model.The traditional naturalgas prediction model has low prediction accuracy and low generalization of the model,so that accurate load prediction cannot be performed.In order to overcome the model defects,a natural gas load forecasting model based on wavelet neural network optimized by genetic algorithm is proposed.The genetic algorithm is used to optimize the parameters of wavelet neural network threshold and network connection weight to establish the best prediction model.The validity,feasibility and accuracy of the prediction model are verified by the historical gate data provided by the enterprise.The simulation results show that the wavelet neural network using genetic algorithm to optimize the network parameters improves the prediction accuracy of the model and has certain engineering application value.