Accurate load forecasting is fundamental to the reliable and economical operation of integrated energy systems, which increasingly combine electricity, heating, and cooling infrastructure. Traditional forecasting methods struggle with two persistent limitations: they fail to extract complex, interdependent features from multi-energy load data, and they demand substantial computational resources that strain practical deployment scenarios.
A new research effort addresses these constraints through a lightweight deep learning framework designed specifically for multi-energy environments. The approach begins by analyzing dynamic coupling relationships between different load types using statistical correlation techniques to optimize input data structure. This analysis ensures that the model captures meaningful interactions rather than treating each energy stream independently.
The core innovation is a variant channel multiscale bottleneck residual network (VC-MBResNet) that efficiently extracts high-dimensional features from load data. Rather than stacking excessive layers that increase computational burden, this architecture uses bottleneck structures and residual connections to maintain feature richness while preserving computational efficiency.
Crucially, the framework implements multi-task learning through both soft and hard parameter-sharing strategies. A bidirectional long short-term memory (BiLSTM) network serves as the foundation, with integrated attention mechanisms that allow the model to focus dynamically on the most influential temporal patterns. This selective attention proves especially valuable in integrated systems where different energy types respond differently to weather, occupancy, and operational changes.
Experimental validation demonstrates that this approach outperforms existing benchmarks in prediction accuracy while requiring significantly less computational overhead—a critical advantage for utilities operating on limited hardware infrastructure. The lightweight design makes the method practical for real-world deployment in diverse settings, from district energy systems to campus microgrids.
These advances have immediate implications for optimal scheduling algorithms and real-time energy management systems. Better forecasting enables more efficient resource allocation, reduced operational costs, and improved grid stability across integrated energy networks.
