通过使用移动平均法和人工神经网络检查占用率和天气参数来预测办公楼的能源消耗
Ali Maboudi Reveshti1, Elham Khosravirad2, Ahmad Karimi Rouzbahani3
1Department of Mechanical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
Heliyon
|February 26, 2024
概括
建筑物占用率显著影响能源使用. 这项研究发现,人工神经网络准确地预测了随着占用率的增加而增加的能源消耗,优于移动平均方法.
科学领域:
- 建筑能源管理 建筑能源管理
- 能源系统中的人工智能
- 占用感应技术 占用感应技术
背景情况:
- 建筑占用率是能源消耗的关键驱动因素,影响照明,HVAC和插头负载.
- 精确的占用监测对于优化建筑能效至关重要.
- 传统的方法可能无法完全捕捉占用率和能源使用之间的动态关系.
研究的目的:
- 调查建筑物占用率与能源消耗之间的相关性.
- 评估人工神经网络 (ANN) 基于占用率的建筑能耗的预测精度.
- 将ANN的性能与统计移动平均值方法进行比较.
主要方法:
- 在办公楼的入口处安装了一个进出口敏感摄像头,以监控占用率.
- 收集的建筑能源消耗数据.
- 开发并应用了人工神经网络模型和移动平均模型,以根据占用数据预测能源消耗.
主要成果:
- 人工神经网络模型实现了4.5%的预测误差.
- 移动平均线的统计方法导致预测误差为9.8%.
- 观察到一种直接的相关性:占用率的增加导致预测能耗的增加.
结论:
- 人工神经网络提供了一种更准确的方法来预测受占用影响的建筑能耗.
- 占用率监测是加强建筑物能源效率战略的宝贵工具.
- 这些发现支持将实时占用数据集成到建筑能源管理系统中.
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