复杂的人工智能模型用于教育建筑中的能源可持续性
Rasikh Tariq1, Awsan Mohammed2,3, Adel Alshibani4,5
1Institute for the Future of Education, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, NL, Mexico. rasikhtariq@tec.mx.
Scientific reports
|July 1, 2024
概括
人工智能模型预测学校的能源消耗,发现建筑物大小和交流电容量是关键因素. 梯度提升和LSTM模型在优化教育设施的能源效率方面表现最好.
科学领域:
- 环境科学 环境科学
- 建筑科学 建筑科学
- 计算机科学 计算机科学
背景情况:
- 教育设施占能源消耗和碳排放的很大一部分.
- 优化学校的能源消耗对于可持续发展至关重要.
- 了解影响学校能源使用的因素对于有针对性的干预措施至关重要.
研究的目的:
- 研究人工智能 (AI) 模型来预测学校的能源消耗.
- 确定影响教育建筑年度能源使用的关键变量.
- 为了比较不同的人工智能模型在能源消耗预测中的性能.
主要方法:
- 开发和评估了四种人工智能模型:决策树,K-最近邻居,梯度增强和长期记忆 (LSTM) 网络.
- 分析了输入参数 (如学校规模,交流电容量) 与年度能源消耗之间的关系.
- 使用训练和测试数据对模型性能进行比较,重点关注预测错误和可变性处理.
主要成果:
- 学校的大小和空调 (AC) 容量被确定为对能源消耗最有影响的变量.
- "学校类型"与年度能源使用的相关性较弱.
- 梯度提升和LSTM模型在预测能源消耗方面表现出卓越的性能,有效地处理各种数据范围.
- 决策树模型在训练数据上表现良好 (3.58%的误差),而K-Nearest Neighbors表现出很高的误差,这表明过拟合.
结论:
- 人工智能模型,特别是渐变增强和LSTM,可以准确预测教育设施的能源消耗.
- 通过人工智能优化学校的能源使用,有助于经济,社会和环境的可持续性.
- 可持续的教育建筑作为教育工具,促进环境管理和参与可持续性概念.
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