To face the challenges of climate change, the integration of renewable energy sources in the energy-intensive heating sector is a crucial aspect of emission reduction. For an efficient operation of coupling devices such as heat pumps with intermittent sources of renewable energy, accurate heat load forecasts need to be developed and embedded into an operation strategy to enable further decarbonisation of heat generation. Data analysis driven forecasts based on weather data hold the potential of identifying consumption patterns to forecast day-ahead heat demand and have been studied extensively for electricity demand forecasts. However, it remains to be shown how such forecasts can be applied in district heating systems. In this study, we propose a control strategy that utilizes hourly heat load forecasts with a 24-hours rolling horizon. First, we investigate supervised forecasting techniques on three different heat load data sets. The application of convolutional neural networks on data of the district heating network in Flensburg, Germany delivers the most promising outcome. Elaborating further on this example, we then develop a control strategy and demonstrate how a heat load forecast can be used to improve the utilization of offshore wind generation or reduce energy costs through a heat pump and a heat storage system. Thus, we contribute to the electrification of the heat sector and thereby enable a reduction of carbon emissions.

中文翻译：

---

区域供热网络的运营策略：应用数据驱动的热负荷预测

为了应对气候变化的挑战，将可再生能源整合到能源密集型供热部门是减排的关键方面。为了使耦合设备（例如带有间歇性可再生能源的热泵）高效运行，需要制定准确的热负荷预测并将其嵌入运行策略中，以实现热量产生的进一步脱碳。基于天气数据的数据分析驱动的预测具有识别消耗模式来预测日间热需求的潜力，并且已针对电力需求预测进行了广泛研究。但是，仍有待证明如何将此类预测应用于区域供热系统。在这项研究中，我们提出了一种控制策略，该策略利用了24小时滚动范围内的每小时热负荷预测。第一，我们研究了三种不同热负荷数据集的监督预报技术。卷积神经网络在德国弗伦斯堡的区域供热网络数据中的应用提供了最有希望的结果。在此示例上进一步详细阐述，然后我们开发控制策略，并演示如何通过热泵和储热系统使用热负荷预测来提高海上风力发电的利用率或降低能源成本。因此，我们为热部门的电气化做出了贡献，从而减少了碳排放。在此示例上进一步详细阐述，然后我们开发控制策略，并演示如何通过热泵和储热系统使用热负荷预测来提高海上风力发电的利用率或降低能源成本。因此，我们为热部门的电气化做出了贡献，从而减少了碳排放。在此示例上进一步详细阐述，然后我们开发控制策略，并演示如何通过热泵和储热系统使用热负荷预测来提高海上风力发电的利用率或降低能源成本。因此，我们为热部门的电气化做出了贡献，从而减少了碳排放。