数据驱动的自适应GM(1,1) 时间序列预测模型用于热舒适度
Xiaoli Li1,2,3, Chang Xu4, Kang Wang1
1Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
International journal of biometeorology
|June 22, 2023
概括
本研究引入了一个自适应灰色模型 (GM(1,1)) 来预测室内环境的未来热舒适度 (预测平均投票指数). 这种模型通过不断调整时间序列数据以更好地控制气候来提高准确性.
科学领域:
- 建筑环境与能源
- 人类舒适度研究 研究人类舒适度
- 预测建模预测建模
背景情况:
- 预测的平均投票 (PMV) 指数对于评估室内热舒适度至关重要.
- PMV受六个复杂的,非线性环境变量的影响.
- 由于可变的相互依赖性,需要简化PMV计算.
研究的目的:
- 为PMV指数开发一个先进的预测模型.
- 为了提高未来热舒适度预测的准确性.
- 为了促进室内气候系统的积极控制.
主要方法:
- 使用了一个改进的灰色系统预测模型,GM(1,1).
- 引入了一个自适应的GM(1,1) 模型来处理时间序列波动.
- 采用移动窗口方法,以持续调整数据集.
主要成果:
- 适应性GM(1,1) 模型显示PMV的预测准确度提高.
- 与标准方法相比,改进的模型取得了更好的性能等级.
- 该模型有效地解决了PMV时间序列的不规则性和不确定性.
结论:
- 适应性GM(1,1) 模型为未来的PMV预测提供了可靠的方法.
- 这项研究支持智能家居中的先进,以人为中心的气候控制.
- 准确的PMV预测可以预先调整空调以获得最佳舒适度.
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