This study proposes a novel machine learning-based methodology to estimate the air-conditioning (AC) load from the hourly smart meter data. The commonly employed approaches for disaggregating the share of the AC load from the total consumption are either using data obtained from dedicated sensors or high-frequency data that cannot be provided by conventional smart meters. In the present work, an alternative approach is proposed, in which a machine learning-based pipeline is first optimised and trained using the data obtained from a set of households in a period, including both smart meter data and the AC load measurements along with corresponding weather conditions. The obtained optimal pipeline is then utilised to estimate the AC load in another set of buildings in the same period of the year, while providing it with only the smart meter data and weather conditions. As the first step of the pipeline implementation, several features are extracted from the smart meters and weather data. The most promising algorithm is then determined, the corresponding hyper-parameters are optimised and the most influential parameters are finally determined. The proposed method leads to a lower monitoring system's cost, lower user privacy concerns and fewer data processing complexity compared to the conventional energy disaggregation approaches, while providing an acceptable accuracy.

Machine learning based disaggregation of airconditioning loads using smart meter data

Najafi B.;Rinaldi F.;
2020-01-01

Abstract

This study proposes a novel machine learning-based methodology to estimate the air-conditioning (AC) load from the hourly smart meter data. The commonly employed approaches for disaggregating the share of the AC load from the total consumption are either using data obtained from dedicated sensors or high-frequency data that cannot be provided by conventional smart meters. In the present work, an alternative approach is proposed, in which a machine learning-based pipeline is first optimised and trained using the data obtained from a set of households in a period, including both smart meter data and the AC load measurements along with corresponding weather conditions. The obtained optimal pipeline is then utilised to estimate the AC load in another set of buildings in the same period of the year, while providing it with only the smart meter data and weather conditions. As the first step of the pipeline implementation, several features are extracted from the smart meters and weather data. The most promising algorithm is then determined, the corresponding hyper-parameters are optimised and the most influential parameters are finally determined. The proposed method leads to a lower monitoring system's cost, lower user privacy concerns and fewer data processing complexity compared to the conventional energy disaggregation approaches, while providing an acceptable accuracy.
2020
energy consumption
learning (artificial intelligence)
power engineering computing
air conditioning
smart meters
data privacy
buildings (structures)
machine learning-based pipeline
AC load measurements
corresponding weather conditions
weather data
fewer data processing complexity
conventional energy disaggregation approaches
machine learning based disaggregation
air-conditioning load
hourly smart meter data
high-frequency data
conventional smart meters
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1160744
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