Open AccessComparative Analysis of Two Methods of Natural Gas Demand Forecasting
Author(s) -
Min Jiang,
Yizhe Dong,
Fang Wu,
Naijie Li,
Hua Wang
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/632/3/032033
Subject(s) - natural gas , hilbert–huang transform , artificial neural network , beijing , environmental science , meteorology , winter season , correlation coefficient , statistics , computer science , mathematics , engineering , climatology , artificial intelligence , geology , geography , energy (signal processing) , waste management , archaeology , china
Based on the monthly dataset of natural gas demand and meteorological factors in heating season (November to next March) of Beijing from 2002 to 2013, an analysis of meteorological condition of natural gas fluctuation is done, which used by Empirical Mode Decomposition (EMD) and statistical correlation methods. A novel intelligent prediction model (EMD_BP) of natural gas demand in heating season via EMD and Back Propagation (BP) neural network algorithm is proposed. In addition, we adopt a prediction model only using BP neural network algorithm compare to the above combine method. The results are as follows, (1) EMD method is a better approach to decompose the social demand and meteorological demand of natural gas. (2) There is a close affinity between natural gas demand and meteorological factors in heating season. The meteorological demand of natural gas is significantly negatively correlated with the mean temperature (also minimum temperature, maximum temperature and negative accumulated temperature), but significantly positively correlated with rainfall and low temperature (<-8°C) days. (3) Comparing with BP model, EMD_BP model can accurately fit the changing tendency of natural gas time series. The results show that the EMD_BP prediction model has better applicability and extensive popular prospect.