Council on Energy, Environment and Water Integrated | International | Independent
Book Chapter

Smart Meter Data Enabled Transition to Energy Efficient Cooling

Shalu Agrawal, Nipun Batra, Karthik Ganesan, Kavita Vaishanaw and Sunil Mani
May 2022 | Power Markets, Sustainable Cooling

Suggested citation: Agrawal, S., Batra, N., Ganesan, K., Vaishanaw, K., Mani, S. (2022). Smart Meter Data-Enabled Transition to Energy Efficient Cooling. In: Pillai, R.K., Ghatikar, G., Sonavane, V.L., Singh, B.P. (eds) ISUW 2020. Lecture Notes in Electrical Engineering, vol 847. Springer, Singapore.


This study uses high frequency smart meter data to demonstrate how power utilities can identify customers driving the peak demand and respond suitably. It also estimates the potential annual energy and cost savings that could accrue by switching to an energy-efficient air-conditioner (AC), and how utilities can share such information with consumers to support optimal decision making. The study uses the data from 93 smart meters from the summer months of 2019, which were deployed in a sample of urban households across two towns in Uttar Pradesh, India.

In India, residential electricity consumption accounts for a quarter of the country’s total consumption. With increasing disposable income and living standards, and warming conditions, the demand for cooling — through the use of ACs — is expected to rise and with it the peak demand for electricity. Therefore, it is important to understand the changing consumption patterns and the drivers of peak demand to find ways of managing this rising demand.

Key Highlights

  • High frequency smart meter data makes it easier to identify households owning and using ACs, as AC ownership is the key differentiating factor for variation in overall consumption.

Households owning ACs consume significantly higher electricity than those without ACs

clean cooking fuels
Source: Authors’ analysis
  • Consumers with high/very-high electricity consumption (87 per cent of whom are also AC users) drive the overall night peak demand, which is also the true daily peak for most households.

Households lying in the high and very-high demand clusters are driving the night peak demand

clean cooking fuels
Source: Authors’ analysis
  • During August 2019, sampled households used AC for an average of 7 hours a day, which varied widely across households (2-12 hours/day). Thus, benefits from any demand response measures would vary with consumers’ appliance usage behaviour.
  • Most sampled households (60 per cent) underestimate their AC use on average by 4 hours, while the rest over-estimated usage, typically by 2.5 hours. Most people tend to wrongly estimate their energy consumption, and hence have poor data to optimise their usage.

Key Recommendations

  • Build analytical capacity within the power utilities to leverage India’s transition towards smart meters and utilise their high frequency data to improve the understanding of the energy consumption pattern of Indian households.
  • Develop a customised consumer engagement approach by identifying and observing power consumption of each of the appliances (using smart meter data) to generate greater consumer buy-in for demand response measures such as adopting high efficiency ACs and using ACs responsibly. Power utilities must target customers where most savings could be made through advisories and real-time feedback on energy usage patterns.
As India rapidly switches to smart electricity meters, power utilities must leverage this opportunity to better understand their consumers’ power demand and proactively nudge consumers towards energy-efficient cooling appliances for effective energy and load management.

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