Equipment- and Time-Constrained Data Acquisition Protocol for Non-Intrusive Appliance Load Monitoring

Konstantinos Koasidis, Vangelis Marinakis, Haris Doukas, Nikolaos Doumouras, Anastasios Karamaneas and Alexandros Nikas ()
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Konstantinos Koasidis: Energy Policy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Vangelis Marinakis: Energy Policy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Haris Doukas: Energy Policy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Nikolaos Doumouras: Energy Policy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Anastasios Karamaneas: Energy Policy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Alexandros Nikas: Energy Policy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece

Energies, 2023, vol. 16, issue 21, 1-26

Abstract: Energy behaviours will play a key role in decarbonising the building sector but require the provision of tailored insights to assist occupants to reduce their energy use. Energy disaggregation has been proposed to provide such information on the appliance level without needing a smart meter plugged in to each load. However, the use of public datasets with pre-collected data employed for energy disaggregation is associated with limitations regarding its compatibility with random households, while gathering data on the ground still requires extensive, and hitherto under-deployed, equipment and time commitments. Going beyond these two approaches, here, we propose a novel data acquisition protocol based on multiplexing appliances’ signals to create an artificial database for energy disaggregation implementations tailored to each household and dedicated to performing under conditions of time and equipment constraints, requiring that only one smart meter be used and for less than a day. In a case study of a Greek household, we train and compare four common algorithms based on the data gathered through this protocol and perform two tests: an out-of-sample test in the artificially multiplexed signal, and an external test to predict the household’s appliances’ operation based on the time series of a real total consumption signal. We find accurate monitoring of the operation and the power consumption level of high-power appliances, while in low-power appliances the operation is still found to be followed accurately but is also associated with some incorrect triggers. These insights attest to the efficacy of the protocol and its ability to produce meaningful tips for changing energy behaviours even under constraints, while in said conditions, we also find that long short-term memory neural networks consistently outperform all other algorithms, with decision trees closely following.

Keywords: energy disaggregation; non-intrusive load monitoring; LSTM-RNN; decision trees; smart meters; time series analysis; energy efficiency; energy policy; smart buildings (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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