通过人工智能驱动的分布式测量系统在教育大楼中对电量进行全面预测
Virginia Negri1, Roberto Tinarelli1, Lorenzo Peretto1
1Department of Electrical, Electronic and Information Engineering, Guglielmo Marconi Alma Mater Studiorum, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究介绍了用于教育建筑的AI驱动的智能基础设施,以改善能源管理. 该系统可靠地预测电气参数,从而实现最佳的能源使用和可扩展性.
科学领域:
- 可持续的能源系统可持续的能源系统
- 电网中的人工智能
- 建筑能源管理 建筑能源管理
背景情况:
- 越来越多的环境问题需要在所有部门,包括电力系统的可持续解决方案.
- 建筑物越来越多地被设计成净零排放或改装以提高能源效率.
- 有效的能源管理对于优化电力系统性能和减少环境影响至关重要.
研究的目的:
- 通过智能基础设施增强教育建筑的能源管理能力.
- 实施和验证人工智能驱动的框架,用于预测电气参数.
- 探索人工智能模型在基础设施内的不同测量节点的概括性.
主要方法:
- 安装光伏电池板和分布式测量系统,以收集电压,电流和功率数据.
- 开发和应用人工智能驱动的数据管理和预测框架.
- 人工智能模型的实验验证和收集数据的相关性分析.
主要成果:
- 实施的系统提供了可靠的电气参数预测.
- 分布式测量系统和数据分析为能源优化提供了宝贵的见解.
- 人工智能模型展示了跨测量节点的概括能力,即使有数据缺口.
结论:
- 智能基础设施有效地支持信息化行动,以优化能源管理和系统性能.
- 该研究强调了准确预测的潜力,并强调了扩展性部署的容易性.
- 这些发现支持在类似的基础设施中适应该系统,以提高能源效率.
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