A Parameter Classification Prediction Method Applied to LEAP Model of Electric Energy Substitution Forecasting

Abstract

As an energy substitution analysis & prediction tool with flexible parameter structure, LEAP model can provide strong support and guidance for guiding the electricity substitution work. On the basis of accurate parameters in LEAP model, this paper proposes a specific parameter classification prediction method. First, a targeted data structure is established, and the parameters that need to be input into the LEAP model are classified into general parameters and scenario parameters according to their degree of certainty. Secondly, predict the general parameters using improved GM(1,1) model by modifying background values and initial conditions. Thirdly, a Grey-Monte Carlo model was proposed to predict scenario parameters and their occurrence probability. Finally, the correctness of the parameter classification and the parameter prediction model are verified through example analysis, and it is proved that the co-application of them improves the accuracy of the parameters and further improves the accuracy of the electric energy substitution prediction.

Decomposition analysis on factors affecting electricity substitution in Guangdong province, China

“…Many factors may affect the process of electricity substitution, such as technological progress, economic development, energy consumption, environmental requirements, policy support, and user willingness [11, 12]. Liu et al deem that the increase in electricity substitution is closely related to population, wealth, technology development, and policy support [13].…”

“…Jing et al [14] consider that the substitution work is affected by energy consumption, GDP, energy prices, investment in renewable power assets and average concentration of P.M. 2.5 and other factors. Cao et al [12] select the macro influence factors including GDP, industrial structure, population, and urbanisation rate to predict electricity substitution quantity by using LEAP prediction method. Chi et al [1] select urbanisation rate, total energy consumption, per capita GDP and carbon dioxide emissions as the influence factors to quantitatively analyse the process of electricity substitution of Beijing.…”