Gas Consumption Classification and Short-term Peak Demand Forecasting of Natural Gas Users in a Certain Province Based on a Series of Intelligent Algorithm
In China,natural gas pipeline network is quite complex due to the large number of users,so the operation and scheduling of the pipeline network is faced with great challenges under the condition of unclear gas consumption habits and fuzzy peak regulating demand.In this paper,K-means++algorithm was used to preliminarily classify the gas consumption habits of different users based on the gas consumption data of the users in a natural gas pipeline network of a certain province from 2018 to 2020.Then,BP neural network was used to learn and classify the feature vectors of typical users,and three types of users were obtained by combining supervised learning and unsupervised learning.Based on the above classification results,the peak regulating demand of users was predicted by seven intelligent algorithm models by using the peak regulating demand data of the typical users for three years,aiming to obtain a reliable short-term prediction model.The model calculation results show that all the seven algorithms have reliable performance for short-term peak regulating prediction,among which the GDB model has the fastest calculation speed and highest accuracy,and has certain feasibility for long-term peak regulating prediction.
peak regulating predictionnatural gas user classificationclustering analysisneural networks
NETL
NSTL
万方数据
