Edge AI for Appliance Identification and Energy Disaggregation on Indian Load Profiles

Abstract

Non-intrusive load monitoring (NILM) research has predominantly focused on load profiles from North American and European households, where appliance mixes, usage patterns, and electrical characteristics differ substantially from Indian consumption. This paper presents edge-deployable NILM models trained and evaluated on Indian residential and small-commercial load profiles. We collected a 14-month dataset from 230 premises across four Indian cities, capturing appliance-level ground truth through a combination of smart plugs and manual annotation. Our lightweight convolutional architecture, designed for deployment on ARM Cortex-M microcontrollers in smart meters, achieves 82% F1 score for appliance identification across 12 common Indian appliance categories, including several (desert coolers, water pumps, inverter-battery systems) absent from standard NILM benchmarks. We show that models trained on Western datasets achieve only 54% F1 when applied to Indian load profiles without fine-tuning, confirming the need for India-specific training data. The model, training code, and a subset of the anonymized dataset are released as open-source resources.

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