Weber Samanta A, Fischlschweiger Michael, Volta Dirk, Geisler Jens
Chair of Technical Thermodynamics and Energy Efficient Material Treatment, Institute for Energy Process Engineering and Fuel Technology, Clausthal University of Technology, 38678, Clausthal- Zellerfeld, Germany.
Energy and Life Science, University of Applied Sciences Flensburg, 24943, Flensburg, Germany.
Sci Rep. 2025 Aug 14;15(1):29789. doi: 10.1038/s41598-025-15777-0.
With the challenge of district heating network transition as part of the global objective of clean energy, machine learning provides a methodological approach for understanding the relationships between various influencing factors and demand-side properties of district heating networks, which is decisive for reducing losses, enhancing sustainability, and guaranteeing residential comfort. This work focuses on accelerating the application of modern machine learning methods to modeling district heating networks by generating knowledge on feature engineering and selection for newly suggested prediction targets, namely volume flow, supply, and return temperatures, directly at the building level. A systematic workflow for data acquisition, feature engineering, and selecting the most relevant predictors is presented. For this, statistical and machine learning methods are applied to engineer respective features and establish specific interdependencies, including meteorological conditions, human behavioral patterns, and operational parameters, based on a model region in northern Germany. The qualitative results indicate that the highest impact is for temporal predictors and operational features derived from the infeed facility's data, i.e., approximately 15 to 20% of the total predictor relevance. In comparison to studies targeting the heat load and suggesting outside air temperature as the most relevant predictor, it was found that for the herein proposed prediction targets, this feature is of secondary relevance (roughly 6-10%). The findings of this study provide a feature engineering and selection strategy, as well as relevant knowledge gain, which is a prerequisite for efficient modeling of district heating networks based on machine learning in the future.
作为清洁能源全球目标的一部分，区域供热网络转型面临挑战，机器学习提供了一种方法论途径，用于理解区域供热网络各种影响因素与需求侧特性之间的关系，这对于减少损耗、增强可持续性以及保障居民舒适度至关重要。这项工作聚焦于通过直接在建筑层面生成关于新提出的预测目标（即体积流量、供回水温度）的特征工程和选择方面的知识，来加速现代机器学习方法在区域供热网络建模中的应用。本文提出了一个用于数据采集、特征工程以及选择最相关预测变量的系统工作流程。为此，基于德国北部的一个模型区域，应用统计和机器学习方法来设计各自的特征，并建立特定的相互依存关系，包括气象条件、人类行为模式和运行参数。定性结果表明，影响最大的是时间预测变量以及从进料设施数据中得出的运行特征，即约占预测变量总相关性的15%至20%。与针对热负荷且将室外空气温度作为最相关预测变量的研究相比，发现对于本文提出的预测目标，该特征的相关性处于次要地位（约为6%至10%）。本研究的结果提供了一种特征工程和选择策略以及相关的知识收获，这是未来基于机器学习对区域供热网络进行高效建模的先决条件。
