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Disaggregation of household solar energy generation using censored smart meter data
Highlights Solar panels can not be detected from smart meters in over 50% of cases if censoring is not considered. A method is presented to detect the maximum power output of a PV panel and infer PV generation from any smart meter. For the sample data, performance metrics were comparable to state-of-the-art approaches which do not consider censoring. An extension demonstrates how to infer PV capacity for a neighbourhood of houses without local solar irradiance.
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.
Disaggregation of household solar energy generation using censored smart meter data
Highlights Solar panels can not be detected from smart meters in over 50% of cases if censoring is not considered. A method is presented to detect the maximum power output of a PV panel and infer PV generation from any smart meter. For the sample data, performance metrics were comparable to state-of-the-art approaches which do not consider censoring. An extension demonstrates how to infer PV capacity for a neighbourhood of houses without local solar irradiance.
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.
Disaggregation of household solar energy generation using censored smart meter data
Brown, Joe (author) / Abate, Alessandro (author) / Rogers, Alex (author)
Energy and Buildings ; 231
2020-11-13
Article (Journal)
Electronic Resource
English
Disaggregation of household solar energy generation using censored smart meter data
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