在区域供暖网络中,在用户层面为特定预测目标选择特征
Samanta A Weber1,2, Michael Fischlschweiger3, Dirk Volta4
1Chair of Technical Thermodynamics and Energy Efficient Material Treatment, Institute for Energy Process Engineering and Fuel Technology, Clausthal University of Technology, 38678, Clausthal- Zellerfeld, Germany. samanta.weber@tu-clausthal.de.
Scientific reports
|August 15, 2025
概括
机器学习模型通过分析影响因素来模拟区域供暖网络. 时间和运行特征是体积流量和温度的关键预测因素,提高了能源效率和可持续性.
科学领域:
- 能源系统工程 能源系统工程
- 数据科学数据科学数据科学
- 可持续能源 可持续能源
背景情况:
- 区域供暖网络对于清洁能源转型至关重要,但在效率和可持续性方面面临挑战.
- 精确的区域供暖网络建模对于减少能源损失和提高用户舒适性至关重要.
- 机器学习提供了一种强大的方法论方法来理解复杂的影响因素和需求侧属性.
研究的目的:
- 加速机器学习在区域供暖网络建模中的应用.
- 为预测建筑物层面的体积流量,供应和返回温度产生有关特征工程和选择的知识.
- 开发一个系统的数据采集和预测器选择工作流程.
主要方法:
- 应用统计和机器学习方法用于特征工程.
- 从德国北部的一个模型地区获得的数据,包括气象,行为和操作参数.
- 系统地选择了区域供暖网络建模中最相关的预测因素.
主要成果:
- 来自食品设施的时间预测因素和运营特征显示出最高的相关性 (15-20%).
- 室外空气温度,通常是热负荷研究的关键,被发现对拟议的预测目标具有次要意义 (6-10%).
- 建立了各种影响因素和网络性能之间的具体相互依赖关系.
结论:
- 该研究为区域供暖网络建模提供了强大的特征工程和选择策略.
- 这些发现为基于机器学习的这些网络的高效建模提供了至关重要的知识.
- 这种方法是优化区域供暖系统以提高可持续性和效率的先决条件.
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