The paper introduces FM4NILM, a prompt‑programmable foundation model that estimates the power usage of any requested appliance from whole‑home meter data, a natural‑language description, and optional activation examples. Trained on 645k sequences from seven public datasets, the lightweight transformer achieves competitive performance across twelve appliance requests, outperforming seven appliance‑specific baselines on key metrics such as event F1 and active‑window MAE. The model’s design allows new appliance coverage to be added via prompts and examples rather than building separate specialist networks.
By Xudong Wang, Jiacheng Cui, Junyu Xue, Tongxin Li, Guoming Tang
The paper introduces a black-box defense strategy for smart meter data that uses a proxy-guided hierarchical reinforcement learning framework to generate battery-based load-shaping policies. These policies inject realistic yet misleading appliance-level signatures into aggregate power signals, disrupting non-intrusive load monitoring attacks. Experiments on UK-DALE and REDD datasets show significant increases in appliance-level reconstruction error and reductions in attacker F1 scores across multiple unseen NILM models.
By Ruichang Zhang, Mustafa A. Mustafa
The paper introduces a new NILM approach that uses label‑preserving aggregate recomposition and prediction consistency to improve appliance‑level power estimation. By recomposing aggregate windows with only the residual background changed, the method preserves target appliance signals while exposing a new supervisory signal. Experiments on REDD, UK‑DALE, and REFIT show reduced mean absolute error for multiple appliances without adding inference‑time complexity.
By Jiangfeng Liu, Yanfang Fan
The paper surveys energy-aware TinyML research, mapping models, learning approaches, and energy-evaluation methods. It classifies influencing factors such as model architecture, hardware, communication, numerical precision, and input characteristics, and organizes optimization strategies into compression, hardware-aware design, system/communication, and data/processing categories. The study notes limited standardization in experimental platforms and reporting, hindering reproducibility and comparison.
By Clariele Almeida, Renan Beserra, Rafael Moura, Danilo Ara\'ujo, Bruno Nogueira, S\'ergio Chevtchenko, Ermeson Andrade
Ireland’s smart metering programme records electricity use at 30‑minute intervals, which is too coarse to capture domestic appliance use. The authors present a label‑free disaggregation system that splits usage data into nine appliance categories by combining event detection for high‑power loads with questionnaire‑guided estimation. Evaluated on four datasets—including a large Irish smart‑meter dataset of over 4,800 years of use—the hybrid method achieves the lowest whole‑decomposition error and better month‑level performance than two independently developed systems.
By Achal Nanjundamurthy, Rupam Misra, Suzanne Little, Alan F. Smeaton
The paper proposes a holistic architectural and algorithmic framework for sustainable cloud‑fog‑edge systems in smart buildings, emphasizing real‑time responsiveness and reduced environmental impact. It validates resilient data acquisition using affordable Arduino hardware and secure persistence solutions, introduces the lightweight KOptim orchestration framework for dynamic SLA management, and adapts the Generalized Hebbian Algorithm for PCA on low‑power microcontrollers with an iterative learning strategy that preserves data diversity. Experimental results on high‑dimensional sensor data demonstrate that this approach converges to quality comparable to full‑memory processing while operating within strict memory constraints.
By Mamadou Sow, Christophe Cerin