通过深度学习框架推动智能社区的发展,以实现可持续的资源管理.
1School of Humanities and Law, Harbin University, Harbin, Heilongjiang, China.
PloS one
|August 7, 2025
概括
这项研究引入了一个人工智能框架,用于智能社区资源管理,减少了18.7%的消费和16.2%的成本. 它利用深度学习优化能源,水和废物系统,以实现可持续的城市发展.
科学领域:
- 城市规划和资源管理
- 人工智能和机器学习
- 可持续发展 可持续发展 可持续发展
背景情况:
- 由于城市增长,智能社区面临着可持续资源管理 (能源,水,废物) 的挑战.
- 先进的人工智能 (AI) 技术为优化资源管理系统提供了新的解决方案.
研究的目的:
- 开发和评估一个深度学习框架,以优化智能社区资源管理.
- 为了提高预测准确度,优化资源分配,并有效地检测运营问题.
主要方法:
- 该框架集成了长期短期记忆 (LSTM) 网络,卷积神经网络 (CNN) 和自动编码器.
- 使用来自开放数据平台 (阿姆斯特丹,新加坡) 和众包数据的数据.
- 使用MAE,RMSE和R2评估模型性能,并与传统方法进行比较.
主要成果:
- 深度学习框架实现了18.7%的资源消耗减少和16.2%的运营成本下降.
- 在LSTM模型中,水需求预测的MAE为1.8.
- 自动编码器成功检测到异常,F1得分为95.5%.
结论:
- 拟议的AI框架有效地解决了智能社区资源管理的挑战.
- 集成先进的深度学习方法优化了资源利用,提高了可持续城市发展的运营效率.
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