A Parameter Classification Prediction Method Applied to LEAP Model of Electric Energy Substitution Forecasting. (September 2020)

• Record Type: Journal Article
• Authors: Cao, Fang; Qian, Ruixin
• Is Part Of: Journal of physics. Volume 1642(2020)
• Publication Date: 2020-09
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1642/1/012024
• Languages: English
• ISSNs: 1742-6588

Abstract

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.