A Deep Learning Framework for Heat Demand Forecasting using Time-Frequency Representations of Decomposed Features

• URL: http://arxiv.org/abs/2603.01137v1
• Date: Sun, 01 Mar 2026 14:40:13 GMT
• Title: A Deep Learning Framework for Heat Demand Forecasting using Time-Frequency Representations of Decomposed Features
• Authors: Adithya Ramachandran, Satyaki Chatterjee, Thorkil Flensmark B. Neergaard, Maximilian Oberndoerfer, Andreas Maier, Siming Bayer,
• Abstract summary: We propose a novel deep learning framework for day-ahead heat demand prediction.<n>By applying Continuous Wavelet Transform to demand and external meteorological factors, our approach enables Convolutional Neural Networks to learn hierarchical temporal features.<n>The results show a significant advancement, reducing the Mean Absolute Error by 36% to 43% compared to the strongest baselines.
• Score: 3.0294044944521694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: District Heating Systems are essential infrastructure for delivering heat to consumers across a geographic region sustainably, yet efficient management relies on optimizing diverse energy sources, such as wood, gas, electricity, and solar, in response to fluctuating demand. Aligning supply with demand is critical not only for ensuring reliable heat distribution but also for minimizing carbon emissions and extending infrastructure lifespan through lower operating temperatures. However, accurate multi-step forecasting to support these goals remains challenging due to complex, non-linear usage patterns and external dependencies. In this work, we propose a novel deep learning framework for day-ahead heat demand prediction that leverages time-frequency representations of historical data. By applying Continuous Wavelet Transform to decomposed demand and external meteorological factors, our approach enables Convolutional Neural Networks to learn hierarchical temporal features that are often inaccessible to standard time domain models. We systematically evaluate this method against statistical baselines, state-of-the-art Transformers, and emerging foundation models using multi-year data from three distinct Danish districts, a Danish city, and a German city. The results show a significant advancement, reducing the Mean Absolute Error by 36% to 43% compared to the strongest baselines, achieving forecasting accuracy of up to 95% across annual test datasets. Qualitative and statistical analyses further confirm the accuracy and robustness by reliably tracking volatile demand peaks where others fail. This work contributes both a high-performance forecasting architecture and critical insights into optimal feature composition, offering a validated solution for modern energy applications.

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