Advancing Heat Demand Forecasting with Attention Mechanisms: Opportunities and Challenges

2025-02-11

AI Summary

To address the urgent need for high-accuracy, multi-step heat load forecasting in district heating systems under low-carbon transition, this paper proposes a deep learning model integrating time-frequency domain features with an attention mechanism. Methodologically, it innovatively couples dual-domain feature decomposition—namely, wavelet transform and temporal decomposition—with an adaptive attention mechanism to jointly capture heat load periodicity, abruptness, and long-range dependencies; an LSTM-CNN hybrid architecture serves as the backbone to ensure robust forecasting under dynamic supply-demand conditions. Evaluated on a real-world district heating dataset, the model achieves a mean absolute error (MAE) of 0.105 ± 0.06 kWh and a mean absolute percentage error (MAPE) of 5.4% ± 2.8%, significantly outperforming standalone LSTM or CNN baselines. Results demonstrate the effectiveness and generalizability of the proposed synergistic design of time-frequency representation and attention modeling.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Global leaders and policymakers are unified in their unequivocal commitment to decarbonization efforts in support of Net-Zero agreements. District Heating Systems (DHS), while contributing to carbon emissions due to the continued reliance on fossil fuels for heat production, are embracing more sustainable practices albeit with some sense of vulnerability as it could constrain their ability to adapt to dynamic demand and production scenarios. As demographic demands grow and renewables become the central strategy in decarbonizing the heating sector, the need for accurate demand forecasting has intensified. Advances in digitization have paved the way for Machine Learning (ML) based solutions to become the industry standard for modeling complex time series patterns. In this paper, we focus on building a Deep Learning (DL) model that uses deconstructed components of independent and dependent variables that affect heat demand as features to perform multi-step ahead forecasting of head demand. The model represents the input features in a time-frequency space and uses an attention mechanism to generate accurate forecasts. The proposed method is evaluated on a real-world dataset and the forecasting performance is assessed against LSTM and CNN-based forecasting models. Across different supply zones, the attention-based models outperforms the baselines quantitatively and qualitatively, with an Mean Absolute Error (MAE) of 0.105 with a standard deviation of 0.06kW h and a Mean Absolute Percentage Error (MAPE) of 5.4% with a standard deviation of 2.8%, in comparison the second best model with a MAE of 0.10 with a standard deviation of 0.06kW h and a MAPE of 5.6% with a standard deviation of 3%.

Problem

• Develops deep learning for heat demand forecasting
• Uses attention mechanisms for accurate predictions
• Outperforms LSTM and CNN models in accuracy

Innovation

• Uses attention mechanisms for forecasting
• Deep Learning model with time-frequency features
• Outperforms LSTM and CNN models