Open AccessAnalysis and Research on Forecasting Electricity Demand Based on ARMA and VAR Model
Author(s) -
Liping Yang,
Jinhui Pang
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 - 10.1088/1755-1315/804/4/042008
Subject(s) - electricity , granger causality , electricity demand , economics , econometrics , unit root , vector autoregression , unit root test , industrialisation , china , autoregressive integrated moving average , urbanization , autoregressive–moving average model , cointegration , electricity generation , autoregressive model , time series , power (physics) , engineering , statistics , mathematics , economic growth , market economy , quantum mechanics , electrical engineering , physics , political science , law
This paper uses the relevant data from the official website of the National Bureau of Statistics of China from 1980 to 2017, and uses the ARMA model and VAR-based co-integration analysis to predict the electricity demand from 2018 to 2020, making the forecast data more convincing. Besides, this paper uses unit root test, Granger causality analysis, Johansen test and other co-integration analysis methods to empirically study the determinants of China’s electricity demand, and establish a longterm equilibrium and its fluctuation relationship between electricity demand and GDP, urbanization rate, industrialization rate, and electricity use efficiency. The forecast results were compared and analyzed, and corresponding policy recommendations were put forward to provide data support and reference for the future development of China’s power industry.