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<p>This study presents a year-long, multi-sensor residential monitoring dataset from a composite-climate home in Mohali, India, integrating appliance-level electricity use, indoor temperature–humidity measurements, and outdoor weather observations into an hourly residential energy informatics workflow. Nine appliances were monitored at 5-minute resolution and processed through a reproducible pipeline involving timestamp harmonization, anomaly handling, multi-source alignment, and feature extraction, yielding an analytics-ready dataset spanning 8,736 hourly observations (July 2024–June 2025). The monitored appliances consumed 845.25 kWh (58% of total household electricity use), with the refrigerator (21.95%) and air conditioner (cooling + heating; 19.07%) as the dominant monitored end uses. Psychrometric mapping quantified mixed-mode operation, distinguishing operating regions for Air Conditioner (AC) cooling (n=460 hours), evaporative cooling (n=172 hours), and AC heating (n=45 hours). Weather sensitivity analysis showed strong temperature dependence in baseline and seasonal loads (effect of outside temperature on refrigerator r=0.793; effect of outside temperature on geyser r=-0.694; slope -0.048 kWh/<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(^{\circ }\)</EquationSource> </InlineEquation>C), demonstrating the value of fusing weather variables into appliance analytics. Indoor comfort evaluation using the India Model for Adaptive Comfort (IMAC) indicated that 80.8% of summer hours and 51.7% of monsoon hours with valid measurements fell within adaptive comfort limits, with winter interpretation constrained by modeling assumptions and sensor availability. The primary contribution is a transparent, transferable workflow that converts heterogeneous residential sensing streams into a unified analytics dataset suitable for season-responsive appliance modeling, comfort-aware interpretation, and future multi-home deployments.</p>

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A multi-sensor informatics workflow for appliance-level energy use, comfort, and mixed-mode cooling in a composite-climate home

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月26日

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  • Sahil Chilana,
  • Aviruch Bhatia,
  • Vishal Garg,
  • Jyotirmay Mathur

摘要

This study presents a year-long, multi-sensor residential monitoring dataset from a composite-climate home in Mohali, India, integrating appliance-level electricity use, indoor temperature–humidity measurements, and outdoor weather observations into an hourly residential energy informatics workflow. Nine appliances were monitored at 5-minute resolution and processed through a reproducible pipeline involving timestamp harmonization, anomaly handling, multi-source alignment, and feature extraction, yielding an analytics-ready dataset spanning 8,736 hourly observations (July 2024–June 2025). The monitored appliances consumed 845.25 kWh (58% of total household electricity use), with the refrigerator (21.95%) and air conditioner (cooling + heating; 19.07%) as the dominant monitored end uses. Psychrometric mapping quantified mixed-mode operation, distinguishing operating regions for Air Conditioner (AC) cooling (n=460 hours), evaporative cooling (n=172 hours), and AC heating (n=45 hours). Weather sensitivity analysis showed strong temperature dependence in baseline and seasonal loads (effect of outside temperature on refrigerator r=0.793; effect of outside temperature on geyser r=-0.694; slope -0.048 kWh/ \(^{\circ }\) C), demonstrating the value of fusing weather variables into appliance analytics. Indoor comfort evaluation using the India Model for Adaptive Comfort (IMAC) indicated that 80.8% of summer hours and 51.7% of monsoon hours with valid measurements fell within adaptive comfort limits, with winter interpretation constrained by modeling assumptions and sensor availability. The primary contribution is a transparent, transferable workflow that converts heterogeneous residential sensing streams into a unified analytics dataset suitable for season-responsive appliance modeling, comfort-aware interpretation, and future multi-home deployments.