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Nonintrusive Load Monitoring Using Recurrent Neural Networks with Occupants Location Information in Residential Buildings

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  • Myeung-Hun Lee

    (Department of Architectural Engineering, Dankook University, Yongin 448-701, Republic of Korea)

  • Hyeun-Jun Moon

    (Department of Architectural Engineering, Dankook University, Yongin 448-701, Republic of Korea)

Abstract

Nonintrusive load monitoring (NILM) is a process that disaggregates individual energy consumption based on the total energy consumption. In this study, an energy disaggregation model was developed and verified using an algorithm based on a recurrent neural network (RNN). It also aimed to evaluate the utility of the occupant location information, which is nonelectrical information. This study developed energy disaggregation models with RNN-based long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the suggested models was evaluated with a conventional method that uses the factorial hidden Markov model. As a result, when developing the GRU disaggregation model based on an RNN, the energy disaggregation performance improved in accuracy, F1-score, mean absolute error (MAE), and root mean square error (RMSE). In addition, when the location information of the occupants was used, the suggested model showed improved performance and good agreement with the real power and electricity consumption by each appliance.

Suggested Citation

  • Myeung-Hun Lee & Hyeun-Jun Moon, 2023. "Nonintrusive Load Monitoring Using Recurrent Neural Networks with Occupants Location Information in Residential Buildings," Energies, MDPI, vol. 16(9), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3688-:d:1132581
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    References listed on IDEAS

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