使用联合学习的智能建筑的隐私保护和分散的热舒适性预测模型
Sidra Abbas1, Shtwai Alsubai2, Gabriel Avelino Sampedro3,4
1Department of Computer Science, COMSATS University, Islamabad, Sahiwal, Pakistan.
PeerJ. Computer science
|March 4, 2024
概括
本研究介绍了一个联合的深度学习 (FDL) 模型,用于准确的智能建筑能耗预测. FDL方法增强了数据隐私,同时实现了82.40%的准确性,解决了机器学习数据需求.
科学领域:
- 建筑科学 建筑科学
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 热舒适度对于智能建筑至关重要,需要准确的能源消耗预测,以有效管理资源.
- 机器学习 (ML) 模型需要大量的数据,在智能建筑应用中构成隐私风险.
- 现有的ML方法与大规模数据收集固有的数据隐私问题作斗争.
研究的目的:
- 为智能建筑能源消耗预测提出一种新的联合深度学习 (FDL) 架构.
- 为解决与智能建筑中的传统机器学习方法相关的数据隐私问题.
- 通过使用公共数据集来评估拟议的FDL模型的有效性.
主要方法:
- 开发了一个基于深度神经网络 (DNN) 范式的联合深度学习 (FDL) 架构.
- 使用了ASHRAE RP-884数据集,应用了最小-最大规范化和合成少数人过量采样技术 (SMOTE).
- 在两个客户端的数据上训练了DNN模型,每个客户端都进行了本地评估以减轻过度装配.
主要成果:
- 拟议的FDL模型在能源消耗预测中实现了82.40%的准确性.
- 在整个培训过程中,FDL架构成功地保持了数据安全.
- 实验结果验证了FDL方法的效率和隐私保护能力.
结论:
- 联合深度学习为智能建筑中的保护隐私的能源消耗预测提供了可行的解决方案.
- 拟议的FDL-DNN模型在分析智能建筑数据方面表现出高准确性和有效性.
- 这项研究有助于开发智能和安全的智能建筑系统.
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