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NILM-Based Feedback for Demand Response: A Reproducible Binary State-Detection Algorithm Using Active Power
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AI
DOI：10.3390/electricity7010023.png)
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摘要
En 中文
Non-intrusive load monitoring (NILM) can provide actionable feedback for demand response (DR) when direct measurements of device states are unavailable. We propose a reproducible, engineering-oriented pipeline for detecting ON/OFF states of end-use load groups from an aggregated active power time series. The method uses robust hysteresis-based labeling with adaptive thresholds derived from the median and median absolute deviation, followed by compact feature engineering restricted to global active power (GAP). After removing collinear features (|r| > 0.98), permutation importance is used to retain informative predictors. Probabilistic binary classifiers (LGBM, Histogram-based Gradient Boosting, XGBoost, and CatBoost) are trained for each target load, and the decision threshold is optimized via F-beta to balance missed events and false alarms. A post-processing stage stabilizes predictions by smoothing probabilities and suppressing spurious triggers. Model quality is assessed with both sample-wise metrics and event-based metrics that credit the correct detection of switching intervals within a time tolerance. Experiments on the open Individual Household Electric Power Consumption dataset (1-min resolution, 2007-2010) demonstrate that lightweight gradient boosting models, particularly LGBM, deliver reliable and interpretable state estimates suitable for practical DR integration and edge deployment.
Keyword:
non-intrusive load monitoring (NILM)
demand response
load state detection
binary classification
event-based evaluation
active power
feature engineering
LightGBM
edge computing
reproducibility
