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[2602.14267] Cross-household Transfer Learning Approach with LSTM-based Demand Forecasting
Summary
The paper presents DELTAiF, a transfer learning framework that enhances LSTM-based demand forecasting for household hot water consumption, reducing training time and improving accuracy.
Why It Matters
As residential heat pump installations rise, optimizing hot water production is crucial for energy efficiency. This study addresses the scalability challenges of traditional forecasting methods, offering a novel solution that can significantly reduce computational costs while maintaining high accuracy.
Key Takeaways
- DELTAiF framework reduces training time by approximately 67%.
- High predictive accuracy maintained with values between 0.874 and 0.991.
- Transfer learning effectively adapts knowledge from one household to others.
- Scalable hot water demand forecasting can lead to reduced energy waste.
- Regular consumption patterns enhance the effectiveness of the model.
Computer Science > Machine Learning arXiv:2602.14267 (cs) [Submitted on 15 Feb 2026] Title:Cross-household Transfer Learning Approach with LSTM-based Demand Forecasting Authors:Manal Rahal, Bestoun S. Ahmed, Roger Renström, Robert Stener View a PDF of the paper titled Cross-household Transfer Learning Approach with LSTM-based Demand Forecasting, by Manal Rahal and 3 other authors View PDF HTML (experimental) Abstract:With the rapid increase in residential heat pump (HP) installations, optimizing hot water production in households is essential, yet it faces major technical and scalability challenges. Adapting production to actual household needs requires accurate forecasting of hot water demand to ensure comfort and, most importantly, to reduce energy waste. However, the conventional approach of training separate machine learning models for each household becomes computationally expensive at scale, particularly in cloud-connected HP deployments. This study introduces DELTAiF, a transfer learning (TL) based framework that provides scalable and accurate prediction of household hot water consumption. By predicting large hot water usage events, such as showers, DELTAiF enables adaptive yet scalable hot water production at the household level. DELTAiF leverages learned knowledge from a representative household and fine-tunes it across others, eliminating the need to train separate machine learning models for each HP installation. This approach reduces overall training time by ap...
