Ask for Any Appliance: A Prompt-Programmable Foundation Model for Non-Intrusive Load Monitoring

2026-09-19

AI Summary

该研究提出FM4NILM模型，通过自然语言描述和可选激活示例从整体家庭电表数据中估计特定电器的能耗轨迹，解决了非侵入式负载监测中覆盖范围扩展成本高的问题。

Abstract

Non-intrusive load monitoring (NILM) estimates appliance-level consumption from a whole-home meter, but appliance-specific models and fixed output inventories make coverage costly to extend. We present FM4NILM (Foundation Model for NILM), a single prompt-programmable model that estimates a requested appliance's power trajectory from aggregate measurements, a natural-language description, and optional activation exemplars. A lightweight cadence-aware transformer is pretrained by masked reconstruction on 645k sequences from seven public corpora spanning 1-60 s sampling intervals, then aligned with appliance requests using observation-masked losses for partially labeled households. A Bernoulli-lognormal decoder separates activity detection from conditional power estimation. On held-out households and time periods from REDD, UK-DALE, and REFIT, one frozen text-prompted model serves twelve appliance-corpus requests, achieving 0.556 event F1, 0.625 AUPRC, and the lowest active-window MAE (251.8 W) among seven appliance-specific baselines. Streaming score aggregation raises event F1 to 0.582 with a 60 s aggregation delay. In a separate category-held-out evaluation, adding ten activation exemplars raises microwave AUPRC from 0.132 to 0.214 without parameter updates. Input-intervention ablations probe the model's dependence on appliance requests and aggregate measurements. These results demonstrate competitive disaggregation with one shared model and support extending appliance coverage through prompts and examples rather than additional specialist networks.

Problem

• Non-intrusive load monitoring
• Appliance-level consumption
• Coverage extension

Innovation

• prompt-programmable
• non-intrusive load monitoring
• lightweight cadence-aware transformer
• Bernoulli-lognormal decoder