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GDP forecast of accommodation and catering industry in X city based on multi variable grey model —— Energy saving development of accommodation and catering industry

机译:基于多变灰色模型的X城市的住宿和餐饮业GDP预测 - 住宿与餐饮业节能开发

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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.
机译:随着住宿和餐饮业的GDP一般是季度或年度,数据量非常小,因此很难准确预测该市为城市的住宿和餐饮业GDP。然而,一座城市可以通过电消耗准确预测住宿和餐饮业的GDP开发。当地政府可以制定相关政策,以促进住宿和餐饮业和经济的稳定发展,并找到电力消费与该行业的GDP开发关系,以达到低能耗和经济快速增长的目的。 T。本文首先对数据进行了脱敏和归一化处理,然后使用MATLAB通过使用GM(1,N)灰色模型,多个线性回归模型和BP神经网络模型来预测X城市的住宿和餐饮业GDP 。实验结果表明,GM(1,N)灰色模型比数据较少情况下的其他两种方法更准确可靠。本文解决了一个城市中住宿和餐饮业GDP的问题是不准确的。

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