ReCo: When to Relocate Sensor Kits under Deployment Constraints -- A NILM Case Study

Haokun Chen; Yu Tong; Yehai Chen

cs.LGSep 28, 2026

ReCo: When to Relocate Sensor Kits under Deployment Constraints -- A NILM Case Study

Authors: Haokun Chen, Yu Tong, Yehai Chen

Organizations: McMaster University Hamilton, ON, Canada · Shanghai Eneintel Technology Co., Ltd. Shanghai, China

Abstract

Many sensing tasks obtain training labels only by deploying instruments in the field. With a limited number of sensor kits, a collection deadline, and measurement downtime at every move, the collector must repeatedly decide whether to stay at the current site or relocate. We study this decision in non-intrusive load monitoring (NILM), which estimates the power drawn by individual appliances from a home's main meter and is trained on data from homes temporarily fitted with appliance-level sub-meters. In NILM, appliance usage varies with the appliance, season and climate, and the value of new data depends on how diverse the combinations of target operation and background load are. To address this, we propose a constraint-based relocation framework and instantiate it for NILM as ReCo (Relocation by Coverage gain). ReCo counts new operating regimes in a joint target-background feature space, forecasts each home's future gain from the data collected so far, and each night weighs the gain of staying against the gain of moving elsewhere after the downtime. In replayed deployments on the Plegma dataset under two kit counts and two downtime costs, ReCo outperforms fixed-dwell and count-based schedules and a threshold rule using the same metric in every setting. Its advantage is not explained by collecting more days alone and reflects allocating the days to more valuable homes and periods.

Figures & tables

Item	Setting
Data and folds	Plegma washing machine; 11 homes, 4 folds; 2 evaluation and 9 candidate homes per fold
Time grid	10-s samples; 12 two-hour windows of 720 samples per day; a window is valid only if $\geq$ 576 samples have both aggregate and target label and, under the appliance’s event rules, gaps change neither its on/off class nor its number of run starts; otherwise it is unknown
Budget	$T{=}120$ days; $K\in\{1,2\}$ ; $c\in\{1,3\}$ days
Dwell	start-up dwell $h_{0}{=}7$ days at the first home of each kit (ReCo, Threshold); minimum dwell $h_{\min}{=}1$ full day for all methods; decisions at the end of a day
Target run (washing machine)	label power $>$ 50 W; gaps of $\leq$ 110 samples merged; duration $\geq$ 10 min
Off space	background median power, fluctuation and time of day, scaled by 200 W, 100 W and 4 h (circular time); seen if normalized distance $\leq$ 1.0

TABLE I: Implementation settings of the NILM instance

	$K{=}1,\ c{=}1$		$K{=}1,\ c{=}3$		$K{=}2,\ c{=}1$		$K{=}2,\ c{=}3$
Method	F1	MAE	F1	MAE	F1	MAE	F1	MAE
Fixed-7	0.577	11.72	0.508	19.29	0.523	17.45	0.618	10.35
Fixed-14	0.620	10.19	0.625	11.37	0.634	9.10	0.656	9.69
Count-5	0.537	11.81	0.543	13.64	0.573	12.68	0.647	12.22
Count-10	0.595	11.37	0.582	14.29	0.630	9.46	0.637	10.21
Threshold	0.616	9.45	0.608	12.08	0.687	7.60	0.671	8.64

TABLE II: Main results on Plegma washing machine

	$K{=}1,\ c{=}1$			$K{=}1,\ c{=}3$			$K{=}2,\ c{=}1$			$K{=}2,\ c{=}3$
Method	Sw	Dwell	Win	Sw	Dwell	Win	Sw	Dwell	Win	Sw	Dwell	Win
ReCo	9.3	10.7	1207	7.3	11.8	1083	19.3	10.4	2374	11.8	14.8	2203
Fixed-7	14.0	7.1	1127	11.0	7.3	923	28.0	7.1	2267	22.0	7.3	1841
Fixed-14	7.0	14.1	1196	6.0	14.6	1074	14.0	14.1	2417	12.0	14.6	2130
Count-5	12.8	7.8	1124	10.8	7.4	927	23.5	8.5	2267	19.8	8.3	1937
Count-10	6.3	15.6	1198	5.8	15.1	1094	11.5	16.9	2431	10.5	16.7	2183

TABLE III: Collection calendars

Cell	F1 ReCo / Surprise	MAE (W) ReCo / Surprise
$K{=}1,c{=}1$	0.678 / 0.639 ( $+$ 0.039 $\pm$ 0.014)	8.29 / 8.94 ( $-$ 0.66 $\pm$ 0.24)
$K{=}1,c{=}3$	0.685 / 0.690 ( $-$ 0.005 $\pm$ 0.004)	10.73 / 13.02 ( $-$ 2.29 $\pm$ 0.80)
$K{=}2,c{=}1$	0.743 / 0.697 ( $+$ 0.046 $\pm$ 0.007)	7.19 / 7.54 ( $-$ 0.35 $\pm$ 0.07)
$K{=}2,c{=}3$	0.706 / 0.668 ( $+$ 0.038 $\pm$ 0.020)	8.02 / 8.57 ( $-$ 0.55 $\pm$ 0.20)

TABLE IV: Alternative gain metric

	$K{=}1,\ c{=}1$		$K{=}1,\ c{=}3$		$K{=}2,\ c{=}1$		$K{=}2,\ c{=}3$
Setting	F1	MAE	F1	MAE	F1	MAE	F1	MAE
Full ReCo	0.678	8.29	0.685	10.73	0.743	7.19	0.706	8.02
w/o availability corr.	0.599 ( $-$ 0.078 $\pm$ 0.011)	9.78 ( $+$ 1.49 $\pm$ 0.77)	0.618 ( $-$ 0.067 $\pm$ 0.043)	10.28 ( $-$ 0.45 $\pm$ 0.22)	0.686 ( $-$ 0.057 $\pm$ 0.008)	8.53 ( $+$ 1.34 $\pm$ 0.82)	0.622 ( $-$ 0.084 $\pm$ 0.022)	9.44 ( $+$ 1.42 $\pm$ 0.82)
w/o home novelty	0.588 ( $-$ 0.090 $\pm$ 0.057)	9.94 ( $+$ 1.66 $\pm$ 0.27)	0.589 ( $-$ 0.097 $\pm$ 0.024)	12.46 ( $+$ 1.72 $\pm$ 1.01)	0.647 ( $-$ 0.097 $\pm$ 0.015)	8.75 ( $+$ 1.57 $\pm$ 0.79)	0.635 ( $-$ 0.071 $\pm$ 0.045)	9.69 ( $+$ 1.67 $\pm$ 0.98)
w/o adapt. shrink./forget.	0.673 ( $-$ 0.005 $\pm$ 0.005)	9.13 ( $+$ 0.85 $\pm$ 0.55)	0.647 ( $-$ 0.038 $\pm$ 0.016)	11.71 ( $+$ 0.97 $\pm$ 0.46)	0.711 ( $-$ 0.032 $\pm$ 0.018)	8.11 ( $+$ 0.92 $\pm$ 0.51)	0.653 ( $-$ 0.053 $\pm$ 0.026)	8.92 ( $+$ 0.90 $\pm$ 0.45)

TABLE V: Component ablation

Scenario	Cell	F1: ReCo / baseline ( $\Delta\,\pm$ SD)	MAE (W): ReCo / baseline ( $\Delta\,\pm$ SD)
Model $\to$ SGN	$K{=}1,c{=}1$	0.740 / 0.563 (+0.177 $\pm$ 0.008)	3.39 / 6.11 ( $-$ 2.72 $\pm$ 0.56)
	$K{=}1,c{=}3$	0.692 / 0.646 (+0.046 $\pm$ 0.181)	4.96 / 4.14 (+0.82 $\pm$ 0.89)
	$K{=}2,c{=}1$	0.722 / 0.673 (+0.049 $\pm$ 0.045)	3.44 / 4.32 ( $-$ 0.88 $\pm$ 0.30)
	$K{=}2,c{=}3$	0.721 / 0.525 (+0.196 $\pm$ 0.113)	3.83 / 5.66 ( $-$ 1.83 $\pm$ 0.12)
Target $\to$ air conditioner	$K{=}1,c{=}1$	0.695 / 0.530 (+0.165 $\pm$ 0.013)	34.97 / 47.28 ( $-$ 12.31 $\pm$ 1.74)
	$K{=}1,c{=}3$	0.681 / 0.486 (+0.195 $\pm$ 0.023)	38.55 / 53.33 ( $-$ 14.78 $\pm$ 2.31)

TABLE VI: Diversity scenarios

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Multi-Appliance Non-Intrusive Load Monitoring via Label-Preserving Aggregate Recomposition and Prediction Consistency

Jiangfeng Liu, Yanfang Fan

School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830047, China

Accurate Short-Term Electricity Load Forecasting Optimal Power Shutoff Energy Forecasting

Open paper AskPDF arXiv

Non-intrusive load monitoring (NILM) estimates appliance power sequences from aggregate power, but models trained on source households commonly lose accuracy in unseen households. Aggregate power also contains loads from other appliances and measurement error, so predictions may depend on the residual background that co-occurs with source-household targets. Time-aligned submetered measurements and the additive decomposition of aggregate power expose a relation unused by window-wise supervision: an aggregate window can be recomposed by replacing only its residual background while preserving all modeled target-appliance power sequences pointwise. We combine label-preserving aggregate recomposition with prediction consistency. Both windows receive complete power and operating-state supervision. For each appliance, disagreement between the two power predictions is penalized only when both satisfy a fixed reliability criterion and only to the extent that it exceeds a fixed margin. The proposed method is implemented using a multi-appliance architecture with two-stage shared-to-specific mixture-of-experts routing. On REDD, UK-DALE, and REFIT, the proposed method lowers appliance-averaged mean absolute error relative to single-window training from 14.75 to 13.14 W, from 8.88 to 8.51 W, and from 15.83 to 14.55 W. Label-preserving aggregate recomposition and prediction consistency are used only during training, and add no inference-time module or parameter.

Abstract

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