一种数据驱动的发现区域供热热负荷模式的方法
A data-driven approach for discovering heat load patterns in district heating
摘要：
了解客户的热量使用情况对于有效的区域供热运营和管理至关重要。不幸的是，现有的知识客户和他们的热负荷行为是相当稀缺的。大多数以前的研究仅限于小规模的分析，不足以代表整个网络的行为。在这项工作中，我们提出了一种数据驱动的方法，使大规模的自动分析区域供热网络中的热负荷模式，而不需要先验知识。我们的方法聚类的客户配置文件到不同的组，提取他们的代表性模式，并检测不寻常的客户的配置文件偏离显着从他们的组的其余部分。使用我们的方法，我们提出了第一个大规模的，全面的热负荷模式进行案例研究，在六个不同的客户类别连接到两个区域供热网络在瑞典南部的许多建筑物。这1222座建筑物的总建筑面积为340万平方米，2016年使用了1540 TJ热量。结果表明，该方法具有很高的潜力，部署和使用在实践中，以分析和了解客户的用热习惯。
英文摘要：
Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are limited to small-scale analyses that are not representative enough to understand the behavior of the overall network. In this work, we propose a data-driven approach that enables large-scale automatic analysis of heat load patterns in district heating networks without requiring prior knowledge. Our method clusters the customer profiles into different groups, extracts their representative patterns, and detects unusual customers whose profiles deviate significantly from the rest of their group. Using our approach, we present the first large-scale, comprehensive analysis of the heat load patterns by conducting a case study on many buildings in six different customer categories connected to two district heating networks in the south of Sweden. The 1222 buildings had a total floor space of 3.4 million square meters and used 1540 TJ heat during 2016. The results show that the proposed method has a high potential to be deployed and used in practice to analyze and understand customers' heat-use habits.
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