Abstract
Based on the existing grey prediction model, this paper proposes a new grey prediction model (the fractional discrete grey model, FDGM (1, 1, t α )), introduces the modeling mechanism and characteristics of the FDGM (1, 1, t α ), and uses three groups of data to verify its effectiveness compared with that of other grey models. This paper forecasts the building energy consumption in China over the next five years based on the idea of metabolism. The results show that the FDGM (1, 1, t α ) can be transformed into other grey models through parameter setting changes, so the new model has strong adaptability. The FDGM (1, 1, t α ) is more reliable and effective than the other six compared grey models. From 2018 to 2022, the total energy consumption levels of civil buildings, urban civil buildings, and civil buildings specifically in Beijing will exhibit steady upward trends, with an average annual growth rate of 2.61%, 1.92%, and 0.78%, respectively.
Highlights
With the development of the economy and the explosive growth of the global population, the energy consumption levels of countries all over the world are increasing each year
According to the China Building Energy Efficiency Development Report (2020), from 2009 to 2017, the total energy consumption of civil buildings in China increased from 568 million tons of coal equivalent (TCE) to 882 million TCE, with an average growth rate of 5.7%. e proportion of the total energy consumed by civil buildings out of the total terminal energy consumption of the entire society increased from 17.63% to 20.18%
The fractionalorder accumulation (FOA) is combined with the discrete grey prediction model, and the FDGM (1, 1, tα) model is proposed to predict the total energy consumption of civil buildings, urban civil buildings, and civil buildings in Beijing
Summary
With the development of the economy and the explosive growth of the global population, the energy consumption levels of countries all over the world are increasing each year. Erefore, this section takes the total energy consumption of civil buildings as an example to test the accuracy of the proposed model and other grey prediction models. Is shows that the FDGM (1, 1, tα) model has higher accuracy than other grey prediction models in predicting the total energy consumption of civil buildings. Erefore, this section takes the energy consumption of urban civil buildings as an example to test the accuracy of the proposed model and other grey prediction models. Is shows that the FDGM (1, 1, tα) model has higher accuracy than other grey prediction models in terms of predicting the energy consumption of civil buildings. Erefore, this section takes the energy consumption of civil buildings in Beijing as an example to test the accuracy of the proposed model and other grey prediction models. The FDGM (1, 1, tα) model has higher accuracy than other grey prediction models in terms of predicting the energy consumption of civil buildings in Beijing
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