通过循环神经网络 (LSTM) 预测占用状态:多房间环境
Mahamadou Klanan Diarra1, Amine Maniar1, Jean-Baptiste Masson1
1Laboratory of Innovative Technologies (LTI UR 3899), Picardy Jules Verne University, 80000 Amiens, France.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究使用非侵入性传感器和人工神经网络 (ANN) 来预测建筑物占用状态. 该模型准确地估计了乘客的行为,有助于提高能源效率.
科学领域:
- 建筑科学 建筑科学
- 人工智能的人工智能
- 环境监测 环境监测
背景情况:
- 建筑物的能源消耗受到居住者的行为和居住状态的严重影响.
- 精确监测乘客习惯对于能源效率至关重要,但为了避免破坏自然行为,最好采用非侵入性的方法.
研究的目的:
- 开发一种非侵入性模型来确定建筑物占用状态.
- 利用环境数据和人工神经网络 (ANN) 来预测乘客行为模式.
主要方法:
- 采用人工神经网络 (ANN),特别是长期短期记忆 (LSTM) 网络.
- 使用非侵入性传感器收集环境数据,包括二氧化碳度和噪音水平.
- 实施基于规则的先验标签,用于训练预测模型.
主要成果:
- 该模型成功地预测了住宅建筑中的四个不同的占用状态.
- 尽管缺乏直接占用数据,但实现了从78%到92%的有希望的预测准确性.
- 证明了使用环境数据来推断乘客的存在和行为的可行性.
结论:
- 与ANN相结合的非侵入性环境传感提供了一种可行的方法来监测建筑物占用状态.
- 开发的LSTM模型显示了通过基于行为的预测来改善建筑能源管理的巨大潜力.
- 进一步的研究可以完善该模型,以提高精度,并在智能建筑系统中得到更广泛的应用.
相关概念视频
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01:27State Space Representation
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