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
arXiv:2512. 22287v3 Announce Type: replace-cross Abstract: Synthetic appliance data are essential for developing non-intrusive load monitoring algorithms and enabling privacy preserving energy research, yet the scarcity of labeled datasets remains a significant barrier.
By Zikun Guo, Adeyinka. P. Adedigba, Rammohan Mallipeddi
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
LoaDiff is a diffusion-based generative model that produces year-long, sub-hourly smart‑meter electricity consumption time series. It can be conditioned on static household attributes like appliance ownership and dynamic factors such as calendar dates and outdoor temperature. Evaluations on three residential datasets show that LoaDiff generates realistic, diverse load profiles, limits memorization, retains useful information for downstream tasks, and responds coherently to conditioning changes.
By Mariia Baranova, Adrien Petralia, Etienne Le Naour, Nathan Etourneau, Guillaume Hofmann, Themis Palpanas
The paper investigates how the operational value of deep‑learning PV disaggregation depends on deployment regimes—whether the model serves known customers, new customers, future periods, or new regions. Using Ausgrid smart‑meter data and cross‑dataset adaptation to Pecan Street, the authors benchmark several deep sequence architectures and find that deployment regime explains more error variation than model choice. Fine‑tuning on a small number of target‑region households can significantly reduce transfer error, and the benefits of long input windows vary by use case.
By Jack Zheng, Hao Wang
The paper presents compact appliance classifiers for local electricity‑load monitoring under memory and computation constraints. It evaluates adaptive sparse evolutionary training and warm‑start compression on synthetic aggregate windows from household appliance recordings, training 120 convolutional models across four targets and three seeds. Two analyses show that the compressed policy preserves binary accuracy while improving macro F1 to 92.40%, reducing stored weights by 17.36% and active first‑layer connections by 23.93%.
By Jinsong Wang, Kenneth A. Loparo
The study evaluates how well time‑series foundation models (TSFMs) can be adapted for electrical load forecasting. It compares pretrained inference, full fine‑tuning, and low‑rank adaptation (LoRA) across four load aggregation levels, using MOIRAI 1.0 Large as the main backbone and Chronos‑2 as a secondary model. LoRA achieves a 7.5% average NMAE reduction on MOIRAI while updating only about 1% of the parameters, offering a strong accuracy‑efficiency trade‑off, though benefits vary in out‑of‑distribution scenarios.
By Aliullah, Julian Oelhaf, Edouard Fouch\'e, Jessica Deuschel, Christian Bergler, Andreas Maier, Siming Bayer
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 Tsetlin Machine (TM)-based framework for Non‑Intrusive Load Monitoring (NILM) that can run in real time on resource‑constrained microcontrollers. By reformulating NILM as a classification problem, the authors achieve high accuracy—90 % precision and 96 % recall for two appliances, and 77 % precision and 80 % recall for four appliances on the REDD dataset—while keeping the trained model to only 18 KB of flash memory. On an ESP32, the system delivers inference in 0.43 ms, demonstrating its feasibility for privacy‑preserving edge deployment.
By Tianhang Tan, Han Wu, Tousif Rahman, Shengyu Duan, Alex Yakovlev, Rishad Shafik
arXiv:2607. 10720v1 Announce Type: new Abstract: The accelerating shift toward low-carbon power systems, together with the widespread adoption of behind-the-meter technologies such as rooftop solar and electric vehicles, is placing new operational and analytical demands on electricity grids.
By Mohannad Takrouri, Nicolas M. Cuadrado A., Martin Tak\'a\v{c}
The paper presents an empirical benchmark of nine deep learning models for smart meter energy forecasting, evaluating them on two public datasets. It examines how historical input length, prediction horizon, and model architecture affect accuracy, finding that longer historical context improves performance up to a saturation point while accuracy declines with longer horizons. The study also compares computational cost, showing lightweight models achieve similar accuracy to heavier ones, and notes that model choice matters less across most population segments.
ElecLoad is a context‑aware agent that improves electricity load forecasting by adaptively organizing the forecasting process around multiple events. It analyzes each event’s relevance, timing, and interactions with historical load, then decides whether to forecast the entire horizon or partition it into segments. Sub‑agents generate initial forecasts for each segment, and the main agent refines them based on feedback, achieving state‑of‑the‑art performance on several datasets.
By Huajian Zhang, Mingyue Cheng, Xingpeng Gao, Xiaoyu Tao, Zhiding Liu, Jiqian Yang, Zhi Li