Abstract
As the GDP of lodging and catering industry is generally quarterly or annual, the data volume is very small, so it is difficult to accurately predict the GDP of lodging and catering industry in this city for a city. However, a city can accurately predict the GDP development of the lodging and catering industry through electricity consumption. And the local government can formulate relevant policies to promote the stable development of lodging and catering industry and economy, and find the relationship between electricity consumption and GDP development of this industry, so as to achieve the purpose of low energy consumption and fast economic growth.t. This paper first conducted desensitization and normalization processing on the data, and then used Matlab to forecast the GDP of the lodging and catering industry in X City respectively by using GM (1, N) grey model, multiple linear regression model and BP neural network model. The experimental results show that the GM (1, N) grey model is more accurate and reliable than the other two methods in the case of less data. This paper solves the problem that the GDP of accommodation and catering industry in a single city is inaccurate.
Highlights
In recent years, electricity data has attracted more and more attention from economists because of its real-time and high efficiency
Yoo S H and Kwak S Y study seven South American countries the causality between electricity consumption and economic growth between the found that the causal relationship between electricity consumption and economic growth vary from country to country, in some countries, the electricity consumption of one-way directly affect economic growth, in some countries, the mutual influence between electricity consumption and economic relationship [3]; In the study on the relationship between China's electricity consumption and economic growth, Pan Kai found that there is a long-term equilibrium relationship between China's electricity consumption and economic development [4]
Gao Qian and Liu Yunyun et al proposed that the dynamic model could improve the prediction accuracy of GDP of the tertiary industry based on electricity consumption [9]
Summary
Electricity data has attracted more and more attention from economists because of its real-time and high efficiency. Using electricity data to study economic structure and growth trend has become one of the hot topics in modern times. In 2007, Sheng-tung Chen and Hsiao-i Kuo et al studied the relationship between electricity consumption and GDP in ten Asian developing countries and concluded that there was a two-way long-term causal relationship between electricity consumption and economic growth [1]. A.Ciarreta and A.Zarraga investigated the electricity and GDP data of 12 European countries from 1970 to 2007 in 2010 and found that there was a short-term negative causal relationship between electricity consumption and GDP [2]. Li Xinying found in 2012 that the Granger causality between Xinjiang's electricity consumption and Xinjiang's economy is a one-way relationship from GDP to installed capacity and electricity consumption [5]. In 2017, He Yongxiu et al showed that there was a Granger relationship between the total electricity consumption and economic development in Shanxi province, and analyzed the deep relationship between the primary, secondary and tertiary industries and electricity consumption, and put forward constructive suggestions for the industrial structure adjustment of Shanxi
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