In developing countries, majority of the households use overhead water tanks to have running water. These water tanks are exposed to the elements, which usually render the tap water uncomfortable to use, given the extreme subtropical weather conditions. Externally weatherproofing these tanks to maintain the groundwater temperature is short-lived, and only results in a marginal (0.5–1 °C) improvement in tap water temperature. We propose Ashray , an IoT-inspired, intelligent system to minimize the exposure of water to the elements thereby maintaining its temperature close to that of the groundwater. Ashray learns the water demand patterns of a household and pumps water into the overhead tank only when necessary. The predictive, machine learning based, approach of Ashray improves water comfort by up to 8 °C in summers and 3 °C in winters, on average. Ashray is retrofitted into existing infrastructure with a hardware prototyping cost of $27, whereas it can save up to 16% on water heating costs, through reduction in natural gas consumption, by leveraging groundwater temperature. Moreover, we also consider a transiently-powered Ashray which uses the energy harvested from the ambient environment, and propose an intermittent data pipeline to improve its prediction accuracy. The transiently-powered Ashray is suitable for long-term deployment, requires minimal maintenance and delivers approximately the same performance. Ashray has the potential to improve the thermal comfort and reduce energy costs for millions of households in developing countries.

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Emerging countries predominantly rely on room-level air conditioning units (window ACs, space heaters, ceiling fans) for thermal comfort. These distributed units have manual, decentralized control leading to suboptimal energy usage for two reasons: excessive setpoints by individuals and inability to interleave different conditioning units for energy savings. We propose a novel inverted HVAC approach: cheaply retrofitting these distributed units with “on-off” control and providing centralized control augmented with room and environmental sensors. Our binary control approach exploits an understanding of device consumption characteristics and factors this into the control algorithms to reduce consumption. We implement this approach as H awadaar in a prototype 180ft 2 room to evaluate its efficacy over a 7-month period experiencing both hot and cold climates. Through a post analysis, we show that our on-off algorithms are not far from a theoretically optimal approach based on a priori information that precisely knows the optimal control points to minimize consumption. We collect enough evidence to plausibly scale our empirical evaluation, demonstrating countrywide benefits: with just 20% market penetration, H awadaar can save up to 6% of electricity per capita in residential and commercial sectors—resulting in a substantial countrywide impact.