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A Deep Learning Framework for Heat Demand Forecasting using Time-Frequency Representations of Decomposed Features
arXiv – CS AI|Adithya Ramachandran, Satyaki Chatterjee, Thorkil Flensmark B. Neergaard, Maximilian Oberndoerfer, Andreas Maier, Siming Bayer||2 views
🤖AI Summary
Researchers developed a deep learning framework using Continuous Wavelet Transform and CNNs for heat demand forecasting in district heating systems. The model achieved 36-43% reduction in forecasting errors compared to existing methods, reaching up to 95% accuracy in predicting day-ahead heat demand across multiple European cities.
Key Takeaways
- →Novel deep learning framework combines time-frequency analysis with CNNs for superior heat demand prediction accuracy.
- →The model reduces Mean Absolute Error by 36-43% compared to state-of-the-art Transformers and foundation models.
- →Achieved up to 95% forecasting accuracy across annual test datasets from Danish and German cities.
- →Framework effectively tracks volatile demand peaks where traditional models fail.
- →Research contributes to optimizing energy sources and reducing carbon emissions in district heating systems.
#deep-learning#energy-forecasting#cnn#wavelet-transform#district-heating#sustainability#carbon-emissions#predictive-analytics#time-series
Read Original →via arXiv – CS AI
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