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An operational strategy for district heating networks: application of data-driven heat load forecasts

机译：地区供热网络的运营策略：数据驱动热负荷预测的应用

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摘要

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.
机译：要面对气候变化的挑战，可再生能源在能量密集的加热部门中的整合是减少减排的关键方面。对于具有间歇性可再生能源的热泵的耦合装置（例如热泵）的有效操作，需要开发精确的热负荷预测并嵌入到操作策略中以实现热发电的进一步脱碳。数据分析驱动的基于天气数据的预测使得识别消耗模式以预测日期的热量需求，并且已经广泛研究了电力需求预测。但是，仍然可以说明这些预测如何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，该策略利用每小时热负荷预测与24小时的滚动地平线。首先，我们调查三种不同热负荷数据集的监督预测技术。卷积神经网络在德国弗伦斯堡区区供暖网络数据的应用提供了最有前途的结果。然后在该示例中进一步详细说明，然后制定控制策略，并演示热负荷预测如何用于通过热泵和储热系统来改善海上风产生的利用或降低能量成本。因此，我们有助于热扇区的电气化，从而能够降低碳排放。

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