Research on the method of electricity demand analysis and forecasting: the case of China

Abstract

With economic expansion of China having moderated to a "New Normal" phase, concerns on the surplus supply of electricity have accelerated, especially in the northwest and northeast regions. The ongoing power system reform also brings various of uncertainties, posing challenge to power supply and demand balance. Therefore, the accurate estimation of electricity demand is still inevitable and urgent. In this paper, the causality relationship of electricity demand and the selected factors, namely GDP, population, energy structure, industrial structure and urbanization, is examined by using Stationary, Co-integration and Granger Causality Tests. Then the direct and indirect effects of these factors are investigated via a path-coefficient analysis. Finally, in these foundations, an improved Chicken Swarm Optimization based on Stimulated Annealing, namely Stimulated Annealing Chicken Swarm Optimization, is proposed to optimize the weighting factors of three forms of electricity demand models. The Stimulated Annealing Chicken Swarm Optimization not only inherits the advantages of the standard Chicken Swarm Optimization such as uncomplicated principle, handy implementation and robustness to control parameters, but also can avoid premature convergence to improve the ability of finding the best solution. Case study reveals that the Stimulated Annealing Chicken Swarm Optimization has better predictive ability than other benchmark algorithms.