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Nonintrusive disaggregation of residential air-conditioning loads from sub-hourly smart meter data
Highlights Air-conditioning energy use is disaggregated from whole-house energy smart meter data. A/C disaggregation accuracy on 5-min data is comparable to 1-min data. The standard deviation in A/C energy use increase linearly with outdoor temperature. A/C use metrics such as run-times and number of cycles are reported for 88 houses.
Abstract The installation of smart meters has provided an opportunity to better analyze residential energy consumption and energy-related behaviors. Air-conditioning (A/C) use can be determined through non-intrusive load monitoring, which separates A/C cooling energy consumption from whole-house energy data. In this paper, a disaggregation technique is described and executed on 1-min smart meter data from 88 houses in Austin, TX, USA, from July 2012 through June 2013. Nineteen houses were sub-metered to validate the accuracy of the disaggregation technique. The R 2 value between the predicted and actual A/C energy use for the 19 houses was 0.90. The algorithm was then applied to all houses. On average, daily energy use from A/C increased by 25±11kWh between a mild temperature day of 15.5°C (60°F) and a hotter day of 31.5°C (89°F), with an 11kWh increase just during peak hours (14:00–20:00). Average time operated, number of cycles, and A/C fraction of energy were found to increase linearly with outdoor temperature up to 25°C (77°F); a plateau was detected at higher temperatures. The accuracy of A/C disaggregation on 5-min data was found to be comparable to 1-min data. However, 15-min data did not yield accurate results due to insufficient granularity.
Nonintrusive disaggregation of residential air-conditioning loads from sub-hourly smart meter data
Highlights Air-conditioning energy use is disaggregated from whole-house energy smart meter data. A/C disaggregation accuracy on 5-min data is comparable to 1-min data. The standard deviation in A/C energy use increase linearly with outdoor temperature. A/C use metrics such as run-times and number of cycles are reported for 88 houses.
Abstract The installation of smart meters has provided an opportunity to better analyze residential energy consumption and energy-related behaviors. Air-conditioning (A/C) use can be determined through non-intrusive load monitoring, which separates A/C cooling energy consumption from whole-house energy data. In this paper, a disaggregation technique is described and executed on 1-min smart meter data from 88 houses in Austin, TX, USA, from July 2012 through June 2013. Nineteen houses were sub-metered to validate the accuracy of the disaggregation technique. The R 2 value between the predicted and actual A/C energy use for the 19 houses was 0.90. The algorithm was then applied to all houses. On average, daily energy use from A/C increased by 25±11kWh between a mild temperature day of 15.5°C (60°F) and a hotter day of 31.5°C (89°F), with an 11kWh increase just during peak hours (14:00–20:00). Average time operated, number of cycles, and A/C fraction of energy were found to increase linearly with outdoor temperature up to 25°C (77°F); a plateau was detected at higher temperatures. The accuracy of A/C disaggregation on 5-min data was found to be comparable to 1-min data. However, 15-min data did not yield accurate results due to insufficient granularity.
Nonintrusive disaggregation of residential air-conditioning loads from sub-hourly smart meter data
Perez, Krystian X. (author) / Cole, Wesley J. (author) / Rhodes, Joshua D. (author) / Ondeck, Abigail (author) / Webber, Michael (author) / Baldea, Michael (author) / Edgar, Thomas F. (author)
Energy and Buildings ; 81 ; 316-325
2014-06-21
10 pages
Article (Journal)
Electronic Resource
English
Nonintrusive disaggregation of residential air-conditioning loads from sub-hourly smart meter data
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