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/^ 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.
Chilana et al. (Thu,) studied this question.
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