A house vacancy rate analysis method based on gray prediction model of electricity consumption data

Yong He; Zhiyong Zhang; Hang Wei; Jie Li

doi:10.1117/12.3011817

7 December 2023 A house vacancy rate analysis method based on gray prediction model of electricity consumption data

Yong He, Zhiyong Zhang, Hang Wei, Jie Li

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294121 (2023) https://doi.org/10.1117/12.3011817
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

With the development of the economy, the vacancy rate of housing in underdeveloped areas of China exhibits characteristics of uneven distribution and seasonal variation, which poses difficulties in accurately assessing the health status of the real estate market. The widespread use of smart meters has made it possible to collect accurate electricity consumption data. In this paper, based on a large amount of electricity consumption data, we propose a discrimination and prediction model for the vacancy rate of houses in Guangxi Province and utilize this model to predict the vacancy rate, providing reliable evidence for evaluating the health status of the real estate market.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yong He, Zhiyong Zhang, Hang Wei, and Jie Li "A house vacancy rate analysis method based on gray prediction model of electricity consumption data", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294121 (7 December 2023); https://doi.org/10.1117/12.3011817

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