Predict-IT - Forecasting District Heating Loads

An Open-Source and User-Friendly Neural Network-Powered Platform


  • Léo Bonal V-Research GmbH
  • Marnoch Hamilton-Jones Graz University of Technology image/svg+xml
  • Zahra Nasrollahinayeri V-Research GmbH
  • Katharina Dimovski V-Research GmbH
  • Doris Entner V-Research GmbH
  • Philip Ohnewein AEE Institute for Sustainable Technologies image/svg+xml
  • Harald Trinkl GET Güssing Energy Technologies



Heat Load Forecast, LSTM-Based Neural Network, Web-Based Platform, Open-Source Software


In the realm of many thermal energy systems, and particularly within district heating networks, heat load forecasts play a pivotal role in optimizing system operation and efficient infrastructure usage. While district heating operators routinely log measurement data, its potential remains underutilized. One essential application of such data is forecasting a network’s heat load based on historical data records. Such forecasts can improve the efficient usage of plant infrastructure and facilitate predictive operational strategies. This paper introduces ”Predict-IT”, a web-based platform designed to standardize the entire forecasting pipeline, making the generation of predictions largely independent of expert knowledge. The Predict-IT platform is powered by a state-of-the-art long short-term memory (LSTM) based neural network algorithm which only requires very little inputs (measured heat load and ambient temperature) to deliver satisfying forecasting accuracy, even a couple of days ahead. The prediction algorithm is validated on two data sets from local Austrian district heating networks, showing the general applicability of the LSTM-based neural network, given an appropriate set of hyperparameters. The Predict-IT platform simplifies the process of forecasting heat loads into a few discrete steps: data upload, algorithm training, heat load forecast generation, and visualization of forecasts. The source code will be open-source, and deployment and installation will be facilitated by an easily installable Docker solution.


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M. Cui, “District heating load prediction algorithm based on bidirectional long short-term memory network model,” Energy, vol. 254, p. 124 283, 2022. DOI:

L. M. Dang, J. Shin, Y. Li, et al., “Toward explainable heat load patterns prediction for district heating,” Scientific Reports, vol. 13, no. 1, p. 7434, 2023. DOI:

M. Abadi, P. Barham, J. Chen, et al., “TensorFlow: A system for large-scale machine learning,” in Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, ser. OSDI’16, USA: USENIX Association, 2016, pp. 265–283, ISBN: 978-1-931971-33-1.

Python Software Foundation, “Python language reference,” [Online]. Available: (visited on 01/11/2024).

Django Software Foundation, “Django docs,” [Online]. Available: (visited on 01/11/2024).

Docker Inc., “Docker docs,” [Online]. Available: (visited on 01/11/2024).

S. Grosswindhager, A. Voigt, and M. Kozek, “Online short-term forecast of system heat load in district heating networks,” in Proceedings of the 31st International Symposium on Forecasting, Prague, Czech Republic, 2011.

Y. Yu, X. Si, C. Hu, and J. Zhang, “A review of recurrent neural networks: LSTM cells and network architectures,” Neural Computation, vol. 31, no. 7, pp. 1235–1270, 2019. DOI:

Keras, “KerasTuner,” [Online]. Available: (visited on 02/27/2024).

M. Schaffer, T. Tvedebrink, and A. Marszal-Pomianowska, “Three years of hourly data from 3021 smart heat meters installed in Danish residential buildings,” Scientific Data, vol. 9, no. 1, p. 420, 2022, ISSN: 2052-4463. DOI:




How to Cite

Bonal, L., Hamilton-Jones, M., Nasrollahinayeri, Z., Dimovski, K., Entner, D., Ohnewein, P., & Trinkl, H. (2024). Predict-IT - Forecasting District Heating Loads: An Open-Source and User-Friendly Neural Network-Powered Platform. International Sustainable Energy Conference - Proceedings, 1.

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