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基于改进的模糊分析层次和TSE-MLR模型,预测大学的能源消耗:一个案例研究

Xiao Chen¹, Xiaobo Peng², Yanzi Li¹

¹College of Physics and Engineering Technology, Chengdu Normal University, China.

|September 25, 2024

概括

一个新的时间分割能量多重线性回归 (TSE-MLR) 模型准确地预测了大学建筑的能源消耗. 与传统和RNN模型相比,这种先进的方法显著减少了预测错误.

科学领域:

建筑能源管理建筑能源管理
高等教育机构的预测建模.
可持续的校园运营可持续的校园运营

背景情况:

准确预测大学校园建筑的能源消耗对于有效管理至关重要.
现有的模型往往侧重于有限的因素或特定的领域,减少预测的准确性和适用性.
需要先进的模型,整合多个因素,以进行全面的能源预测.

研究的目的:

引入和评估时间分割能源多重线性回归 (TSE-MLR) 模型,用于预测大学建筑的能源消耗.
将TSE-MLR模型的性能与传统 (MLR,BP) 和高级 (RNN) 模型进行比较.
通过改进的模糊分析层次流程,识别影响校园能源使用的关键因素.

主要方法:

开发了TSE-MLR模型,集成了改进的模糊分析层次和多重线性回归.
使用气象和12年历史数据确定主要的能源消耗因素.
使用不同的数据期进行TSE-MLR模型的培训和验证 (2010-2016年用于培训,2017-2019年用于验证).

主要成果:

通过TSE-MLR模型,预测误差显著降低13.8%.
与多重线性回归 (MLR),逆向传播 (BP) 神经网络和循环神经网络 (RNN) 相比,TSE-MLR模型的预测准确度更高.
影响校园能源消耗的关键因素被确定并纳入预测模型.

结论:

该TSE-MLR模型为预测大学能源消耗提供了一种新且有效的方法.
这种方法通过利用历史操作数据来支持增强的能源管理策略.
该研究强调了综合建模技术在提高校园建筑能效方面的潜力.

关键词:

在BP神经网络中,神经网络改进了模糊的分析层次结构.线性回归是一种线性回归.在RNN模型中使用RNN模型.在TSE-MLR模型中,大学能源消耗预测预测

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