TY - CONF A1 - Pressa, Christian A1 - Leiprecht, Stefan A1 - Behrens, Fabian A1 - Jetzinger, Verena A1 - Popma, Hendric A1 - Finkenrath, Matthias T1 - Benchmarking of state-of-the-art machine learning methods for highly accurate thermal load forecasting in district heating networks T2 - 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2023) N2 - Decarbonisation of heat generation has become a priority for district heating network operators. In order to avoid the use of fossil-fired boilers, operators need to know peaks in heat demand in advance. Accurate thermal load forecasting is playing an increasingly important role in this respect. This paper presents the final results of the research project “deepDHC” (deep learning for district heating and cooling) funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK). The three-year project focused on systematically benchmarking thermal load forecasts for district heating networks, based on state-of-the-art machine learning methods. The analysis covers a variety of machine learning techniques, such as neural networks – including latest deep learning methods – (e.g. LSTM, TFT, ESN, RC), decision trees (random forests, adaptive boosting, XGB) and statistical methods (SARIMAX). In addition, the impact of combining methods by so-called “stacking” was investigated. Training and validation of the machine learning algorithms was based on historical operating data from the district heating network for the city of Ulm in Germany, in combination with historical weather data, and weather forecasts. Thermal load forecasts – typically for three days ahead – are presented and compared against one another. An automatic tuning routine was developed as part of the project, which enables regular re-training of the machine learning algorithms based on the latest operating data from the heating network. Furthermore, a web interface for real-time forecasting was developed and implemented at the power station. KW - Maschinelles Lernen KW - Lastprognose KW - Maschinelles Lernen KW - Fernwärme KW - deepDHC Y1 - 2023 UR - https://opus4.kobv.de/opus4-hs-kempten/frontdoor/index/index/docId/2009 UR - https://www.proceedings.com/content/069/069564-0283open.pdf SN - 9781713874928 VL - 2023 SP - 3154 EP - 3162 PB - Curran Associates, Inc. CY - New York ER -