使用数字双胞胎和统计机器学习的协同方法,用于智能住宅能源建模
Ahmad Almadhor1, Shtwai Alsubai2, Natalia Kryvinska3
1Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, 72388, Sakaka, Saudi Arabia. aaalmadhor@ju.edu.sa.
这项研究将数字双胞胎 (DT) 系统与人工智能 (AI) 集成,用于智能建筑能源管理. 该框架准确地根据能源效率对建筑物进行分类,优化消耗和可持续性.
科学领域:
- 建筑能源管理 建筑能源管理
- 人工智能的人工智能
- 数字双胞胎技术是数字双胞胎技术.
背景情况:
- 传统的能源管理方法与动态需求和用户偏好作斗争,导致效率低下.
- 数字化和智能能源管理对于建设能源效率至关重要.
- 现有的数字双胞胎模型通常仅限于可视化或模拟.
研究的目的:
- 提出使用数字双胞胎 (DT) 系统和人工智能 (AI) 进行智能建筑能耗评估的综合框架.
- 开发实时虚拟双胞胎表示,作为适应性,数据驱动的决策工具.
- 通过实时优化和智能控制来提高建筑的能源效率和可持续性.
主要方法:
- 数字双胞胎 (DT) 系统与人工智能 (AI) 算法的集成.
- 利用物联网 (IoT) 传感器数据进行持续反和模型演变.
- 实施机器学习模型,包括随机森林,深度神经网络,长短记忆 (LSTM) 和双向长短记忆 (BiLSTM) 网络.
- 综合数据预处理和功能工程.
主要成果:
- 该系统以98%的准确性准确地将建筑物分为节能和非节能组.
- 与其他评估的AI技术相比,随机森林模型表现出卓越的性能.
- 对培训曲线和特征重要性的分析提供了对模型行为和关键能源效率驱动因素的见解.
- 图形表示说明了DT和AI在优化能源管理和减少浪费方面的有效性.
结论:
- 将实时数字双胞胎环境与智能学习模型相结合,对于应对现代能源效率挑战至关重要.
- 拟议的框架为优化工业和城市环境中的能源管理提供了一种变革性的方法.
- 这项研究支持通过先进的AI和DT集成来开发适应性和可持续的建筑系统.
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