Smart Meter Data Enabled Transition to Energy Efficient Cooling

In India, residential power consumption accounts for a quarter of the country’s total consumption. With increasing disposable income and living standards and indeed warming conditions, demand for cooling through the use of air conditioners, is expected to rise and with it the peak demand. There is a new opportunity to manage the growth of this demand by first identifying the contribution of various end-uses to peak demand and supporting uptake of energy efficient equipment and influencing behavior concerning the use of cooling equipment. The information that is currently available from conventional meters and the frequency of collection is not sufficient to enable such analyses. With the help of high-frequency data from smart meters, electricity utilities can identify customers driving the peak demand and respond suitably. We demonstrate this with the help of data from 93 smart meters deployed in a sample of urban households across two towns in Uttar Pradesh. We use k-means clustering to identify the customer segments driving peak demand; these are primarily AC users. We also demonstrate a simple technique to identify AC using households with demand data at 15-min interval. Thereafter, we demonstrate the use of the current signature, to estimate the hours of AC use, compressor activity rate and overall power consumption. We then estimate the potential annual energy and cost savings that could accrue by switching to a reference efficient appliance offering a similar service level. The analysis, when viewed in aggregate could inform the utility of the need for overall capacity augmentation, the room for peak shifting, in addition to a peak shaving with more efficient appliances.