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DynaSplit: A Hardware-Software Co-Design Framework for Energy-Aware Inference on Edge
While split computing enables the decomposition of large neural networks (NNs) and allows partial computation on both edge and cloud devices, identifying the most suitable split layer and hardware configurations is a non-trivial task.
Decentralized Coordination of Distributed Energy Resources through Local Energy Markets and Deep Reinforcement Learning
This study demonstrates the effectiveness of DRL in decentralized grid management, highlighting its scalability and near-optimal performance in reducing net load variability within community-driven energy markets.
Forecasting Photovoltaic Power Production using a Deep Learning Sequence to Sequence Model with Attention
The proposed model is based on two seminal concepts that led to significant performance improvements of deep learning approaches in other sequence-related fields, but not yet in the area of time series prediction: the sequence to sequence architecture and attention mechanism as a context generator.
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