Edge-Optimized NILM: Combining Structured Pruning and Quantization for Energy Disaggregation
S. Athanasoulias, D. Karagkounis, G. Samaras, and I. Kokos, “Edge-Optimized NILM: Combining Structured Pruning and Quantization for Energy Disaggregation,” in Proceedings of Emerging Tech Conference: Edge Intelligence 2024, vol. 03, 2024, pp. 24-30. DOI: doi.org/10.63438/RXZL7837
Balancing Cost and Self-Consumption in Home Energy Management: A Deep Reinforcement Learning and Model-Based Perspective with EV and Storage Systems
I. Kappos, S. Martinez-Hermida, S. Barja-Martinez, C. Menos-Aikateriniadis and M. Aragüés-Peñalba, “Balancing Cost and Self-Consumption in Home Energy Management: A Deep Reinforcement Learning and Model-Based Perspective with EV and Storage Systems,” 2025 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), Valletta, Malta, 2025, pp. 1-5, doi: 10.1109/ISGTEurope64741.2025.11305403.
Isomorphic structured pruning of temporal CNNs for scalable NILM on edge devices
Sotirios Athanasoulias, Nikos Temenos, Ilias Kappos, Isidoros Kokos, Pedro Antonio Garcia-Abadillo Navaro, Nikolaos Ipiotis, Anastasios Doulamis, Nikolaos Doulamis,
Isomorphic structured pruning of temporal CNNs for scalable NILM on edge devices,
Energy Reports, Volume 14, 2025, Pages 3048-3061, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2025.09.007
Towards edge-computed NILM: Insights from a Mediterranean Use Case
Athanasoulias, Sotiris & Temenos, Nikolaos & Doulamis, Nikolaos & Doulamis, Anastasios & Kokos, Isidoros & Ipiotis, Nikolaos. (2024). Towards edge-computed NILM: Insights from a Mediterranean Use Case. 1-5. 10.1109/SyNERGYMED62435.2024.10799297.