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Home idle load is the continuous residential electric
energy consumption as measured by
smart meters.[1][2][3] It differs from
standby power (loads) in that it includes energy consumption by devices that cycle on and off within the hourly period of standard smart meters (such as
fridges,
aquariumheaters,
wine coolers, etc.).[4][5][6] As such, home idle loads can be measured accurately by smart meters. According to Stanford Sustainable Systems Lab, home idle load constitutes an average of 32% of household electricity consumption in the U.S.[7][8]
Type of devices
The primary categories of devices that contribute to Home Idle Load include:
Any type of device used to maintain a continuous temperature differential (including
freezers,
icemakers,
refrigerators,
wine coolers,
terrariumheaters, heated floors,
instant hot water dispensers, etc.). Although such devices may need to stay on continuously, more recent models have proven to be more efficient and can result in considerably lower home idle loads.[9][10]
Using a
timer switch that stops electric consumption from devices when not in use
Using a smart power strip with a master outlet that manages electricity for multiple devices
Replacing older (or malfunctioning) devices with more efficient options[11][12]
Measuring home idle load
Home idle load may be measured differently depending on the
electric meter and local utility used. A smart meter with a local utility that supports "green button" data is the most accurate option to measure home idle load. Another option involves using the user's utility website to access consumption charts showing hourly electric use. If green button data is not available, the user may measure home idle load by analysing the home's
electric meter while all home electronic devices are in inactive mode. This may involve using a timer to track the time for a single revolution of the spinning dial of an
analog electric meter.[3]
^R. Rajagopal; et al. (2015). "VISDOM: Data Analytics Architecture for Load Management". Stanford Sustainable Systems and Smart Grid Labs at Stanford University.
^Borgeson, S.; Tan, S. (2015). "Learning from Hourly Household Energy Consumption: Extracting, Visualizing, and Interpreting Household Smart Meter Data". Proceedings of the 2015 Human-Computer Interaction (HCI) Conference, Los Angeles.
^Smith, B.A.; Wong, J. (2012). "Simple Way to Use Interval Data to Segment Residential Customers for Energy Efficiency and Demand Response Program Targeting". ACEEE Summer Study Proceedings: 5–374–386.
^Bensch, I.; Pigg, S. (2010). "Electricity Savings Opportunities for Home Electronics and Other Plug-In Devices in Minnesota Homes: A Technical and Behavioral Field Assessment". Energy Center of Wisconsin.
^Chetty, M.; Tran, D (2008). "Getting to green: Understanding resource consumption in the home". Proceedings of the 10th international conference on Ubiquitous computing. pp. 242–251.
doi:
10.1145/1409635.1409668.
ISBN9781605581361.
S2CID22189106.