Journal article
Disaggregation of household solar energy generation using censored smart meter data
- Abstract:
- Quantifying small scale domestic solar (PV) generation from energy consumption is becoming increasingly important as the install base of small solar (PV) panels rapidly grows. Unfortunately, it is often the case that the only insight into the consumption and generation of energy within a house comes from smart-meter readings. The smart meter records the amount of energy the house takes from the grid, and does not independently measure and report the local generation that might be consumed by the home, or fed back to the grid. To address this issue, we propose a novel approach to disaggregate PV generation from energy consumption that also infers installed PV capacity. This is done by disaggregating PV generation from censored smart meter readings, and specifically by finding the most likely distribution for the energy consumption and using it to infer the solar generation. We extend this approach to propose the first technique to infer PV capacity without weather data or a solar proxy, using instead only smart meter readings given a group of houses in close proximity. We evaluate the algorithm on two datasets: (i) the US Pecan Street dataset is adapted so that net energy meter readings are censored; and (ii) a constructed dataset, combining smart meter readings from UK households and solar energy generation from locations across the UK. Our results show comparable accuracy at inferring PV capacity compared to existing approaches, which cannot deal with censored readings which represent over 50% of PV panel installations in the UK.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
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(Preview, Accepted manuscript, 471.7KB, Terms of use)
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- Publisher copy:
- 10.1016/j.enbuild.2020.110617
Authors
- Publisher:
- Elsevier
- Journal:
- Energy and Buildings More from this journal
- Volume:
- 231
- Article number:
- 110617
- Publication date:
- 2020-11-20
- Acceptance date:
- 2020-11-13
- DOI:
- ISSN:
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0378-7788
- Language:
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English
- Keywords:
- Pubs id:
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1150490
- Local pid:
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pubs:1150490
- Deposit date:
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2020-12-23
Terms of use
- Copyright holder:
- Crown Copyright
- Copyright date:
- 2020
- Rights statement:
- Crown Copyright © 2020 Published by Elsevier B.V. All rights reserved.
- Notes:
- This is the accepted manuscript version of the article, available under the terms of a Creative Commons, Attribution, Non-Commercial, No Derivatives licence. The final version is available online from Elsevier at: https://doi.org/10.1016/j.enbuild.2020.110617
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