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Evaluating different artificial neural network forecasting approaches for optimizing district heating network operation

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  • Frison, Lilli
  • Gölzhäuser, Simon
  • Bitterling, Moritz
  • Kramer, Wolfgang

Abstract

Accurate heat demand forecasting is essential for energy-efficient management of district heating networks (DHN), which face complexities such as varying weather, user behavior, and energy availability. This paper evaluates the effectiveness of Artificial Neural Networks (ANN), including recurrent Long Short-Term Memory Networks, Convolutional Neural Networks, and the Temporal Fusion Transformer, against the statistical SARIMAX model. These models are assessed based on their ability to predict diverse heat demand profiles and provide interpretable forecasts with optimization strategies, particularly emphasizing comprehensible confidence intervals. Utilizing a year's data from Stiftung Liebenau DHN, which includes multiple energy sources like CHP, biomass, and natural gas, and varied heat sinks such as residential buildings and greenhouses, we find that despite the CNN model achieving the lowest MAPE of 27 % for summer and winter, and 17 % for winter only, prediction accuracy is significantly affected by data volatility and irregularity. However, the models successfully capture the overall trend but face challenges in accurately predicting demand peaks and fluctuations. An economic analysis indicates that applying these predictive methods significantly enhances energy efficiency and offers economic benefits due to low investment costs.

Suggested Citation

  • Frison, Lilli & Gölzhäuser, Simon & Bitterling, Moritz & Kramer, Wolfgang, 2024. "Evaluating different artificial neural network forecasting approaches for optimizing district heating network operation," Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:energy:v:307:y:2024:i:c:s0360544224025192
    DOI: 10.1016/j.energy.2024.132745
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    References listed on IDEAS

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    1. Yuan, Chongshuo & Lin, Xiaojie, 2025. "Graph-temporal convolutional network for steam heating network simulation considering dynamic characteristics," Energy, Elsevier, vol. 333(C).
    2. Yangluxi Li & Huishu Chen & Peijun Yu & Li Yang, 2025. "A Review of Artificial Intelligence Applications in Architectural Design: Energy-Saving Renovations and Adaptive Building Envelopes," Energies, MDPI, vol. 18(4), pages 1-24, February.
    3. Steinegger, Josef & Stering, Stefan & Kienberger, Thomas, 2025. "Economic feasibility of supra-regional district heating networks: Addressing technical and economic considerations," Energy, Elsevier, vol. 326(C).
    4. Tan, Quanwei & Zhu, Jiebei & Xue, Guijun & Xie, Wenju, 2025. "A hybrid heat load forecasting model based on multistage decomposition and dynamic adaptive loss function," Energy, Elsevier, vol. 335(C).
    5. Andrzej Szymon Borkowski & Patrycja Olszewska, 2025. "BIM Model of District Heating Networks in Design and Investment Management Processes: A Case Study," Sustainability, MDPI, vol. 17(9), pages 1-14, May.
    6. Li, Pengchao & Guo, Fang & Li, Yongfei & Yang, Xuejing & Yang, Xudong, 2025. "Physics-informed neural network for real-time thermal modeling of large-scale borehole thermal energy storage systems," Energy, Elsevier, vol. 315(C).
    7. Shan, Xiaonian & Li, Qi & Wan, Changxin & Ouyang, Ming & Hao, Peng & Wu, Guoyuan & Barth, Matthew, 2025. "An enhanced bilayer long short-term memory method for energy consumption estimation of electric buses with real-time passenger load," Energy, Elsevier, vol. 338(C).

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