Abstract
In recent years, tourism become more popular, and analyzing electricity consumption in tourism industry contributes to its development. To predict energy consumption, this paper applies a new model, NEWARMA model, which means to add the variable’s own medium- and long-term cyclical fluctuations item to the basic ARMA model, and the prediction accuracy will be significantly improved. This paper also compares fitting result of NEWARMA to neural network models and grey models, and finds that it performs better. Finally, through simulation analysis, this study finds that when electricity in one industry declines, other industries may be affected and changed too, which help our country to control total energy consumption in the society.
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Source: State Grid Corporation of China.
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Note: GM(1,1) and BP(1) means using sequence itself to predict it, while GM(1, n) and BP(n) means using n other variables to predict 1 variable, here n equal to 4, including GDP, average temperature, holiday, and \( x_{1} \).
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Acknowledgement
This work is supported by the National Natural Science Foundation of China No. 71501175, the University of Chinese Academy of Sciences, and the Open Project of Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences.
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Huang, Z., Li, Z., Zhang, Y., Guo, K. (2020). Forecasting on Electricity Consumption of Tourism Industry in Changli County. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_9
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DOI: https://doi.org/10.1007/978-981-15-2810-1_9
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