RT Journal Article
A1 Yongxiu He
A1 Fengtao Guang
A1 Rongjun Chen

PB iet
T1 Prediction of electricity demand of China based on the analysis of decoupling and driving force
JN IET Generation, Transmission & Distribution
VO 12
IS 13
SP 3375
OP 3382
AB The emerging complex circumstances caused by the essence of the new economic normal and the low-carbon development of power industry lead to the demand for electricity from the rapid growth phase to a ‘new normal’ pace for China. To better capture the more and more non-linear and non-stationary characteristics of electricity demand, Tapio decoupling model is applied to research the decoupling of electricity demand from economic growth, and T's correlation degree is used to measure the influencing degree and reveal the role direction of each driver on electricity demand. On these bases, a novel improved particle swarm optimisation-extreme learning machine (IPSO-ELM) hybrid forecasting model is proposed to predict electricity demand. In IPSO-ELM, a mutation operator of genetic algorithm is introduced into standard PSO to ensure the overall convergence and enhance the accuracy of convergence, and the improved PSO is adopted to optimise the input weights and hidden bias of traditional ELM which are randomly generated. Finally, the case study of China demonstrated the efficacy and feasibility of IPSO-ELM. Simultaneously, the comparisons with ELM, back propagation neural network, support vector machine, GM (1, 1), and logistic model showed that IPSO-ELM has a better forecasting performance in electricity demand.
K1 support vector machine
K1 nonlinear characteristics
K1 decoupling analysis
K1 low-carbon development
K1 electricity demand prediction
K1 GM (1, 1)
K1 backpropagation neural network
K1 improved particle swarm optimisation-extreme learning machine hybrid forecasting model
K1 China
K1 economic growth
K1 T's correlation degree
K1 driving force analysis
K1 Tapio decoupling model
K1 genetic algorithm
K1 IPSO-ELM
K1 power industry
K1 mutation operator
K1 nonstationary characteristics
K1 logistic model
DO https://doi.org/10.1049/iet-gtd.2017.1493
UL https://digital-library.theiet.org/;jsessionid=2ipyhcdajsiuq.x-iet-live-01content/journals/10.1049/iet-gtd.2017.1493
LA English
SN 1751-8687
YR 2018
OL EN