Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
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.
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
06.01.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
690
Machine learning based disaggregation of airconditioning loads using smart meter data
| BASE | 2020
NON-INTRUSIVE LOAD DISAGGREGATION METHODS FOR LOW-RATE SMART METER DATA
| BASE | 2021