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New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid

Author

Listed:
  • İsmail Hakkı ÇAVDAR

    (Department of Electric-Electronic, Karadeniz Technical University, 61080 Trabzon, Turkey)

  • Vahid FARYAD

    (Department of Electric-Electronic, Karadeniz Technical University, 61080 Trabzon, Turkey)

Abstract

Energy management technology of demand-side is a key process of the smart grid that helps achieve a more efficient use of generation assets by reducing the energy demand of users during peak loads. In the context of a smart grid and smart metering, this paper proposes a hybrid model of energy disaggregation through deep feature learning for non-intrusive load monitoring to classify home appliances based on the information of main meters. In addition, a deep neural model of supervised energy disaggregation with a high accuracy for giving awareness to end users and generating detailed feedback from demand-side with no need for expensive smart outlet sensors was introduced. A new functional API model of deep learning (DL) based on energy disaggregation was designed by combining a one-dimensional convolutional neural network and recurrent neural network (1D CNN-RNN). The proposed model was trained on Google Colab’s Tesla graphics processing unit (GPU) using Keras. The residential energy disaggregation dataset was used for real households and was implemented in Tensorflow backend. Three different disaggregation methods were compared, namely the convolutional neural network, 1D CNN-RNN, and long short-term memory. The results showed that energy can be disaggregated from the metrics very accurately using the proposed 1D CNN-RNN model. Finally, as a work in progress, we introduced the DL on the Edge for Fog Computing non-intrusive load monitoring (NILM) on a low-cost embedded board using a state-of-the-art inference library called uTensor that can support any Mbed enabled board with no need for the DL API of web services and internet connectivity.

Suggested Citation

  • İsmail Hakkı ÇAVDAR & Vahid FARYAD, 2019. "New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid," Energies, MDPI, vol. 12(7), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1217-:d:218124
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Cited by:

    1. Mingzhi Yang & Yue Liu & Quanlong Liu, 2021. "Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning," Sustainability, MDPI, vol. 13(12), pages 1-11, June.
    2. Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano & Saad Dosse Bennani & Hakim El Fadili, 2022. "Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection," Energies, MDPI, vol. 15(3), pages 1-22, February.
    3. Krzysztof Dowalla & Piotr Bilski & Robert Łukaszewski & Augustyn Wójcik & Ryszard Kowalik, 2022. "Application of the Time-Domain Signal Analysis for Electrical Appliances Identification in the Non-Intrusive Load Monitoring," Energies, MDPI, vol. 15(9), pages 1-20, May.
    4. Hari Prasad Devarapalli & V. S. S. Siva Sarma Dhanikonda & Sitarama Brahmam Gunturi, 2020. "Non-Intrusive Identification of Load Patterns in Smart Homes Using Percentage Total Harmonic Distortion," Energies, MDPI, vol. 13(18), pages 1-15, September.
    5. Patrick Huber & Alberto Calatroni & Andreas Rumsch & Andrew Paice, 2021. "Review on Deep Neural Networks Applied to Low-Frequency NILM," Energies, MDPI, vol. 14(9), pages 1-34, April.
    6. Jiateng Song & Hongbin Wang & Mingxing Du & Lei Peng & Shuai Zhang & Guizhi Xu, 2021. "Non-Intrusive Load Identification Method Based on Improved Long Short Term Memory Network," Energies, MDPI, vol. 14(3), pages 1-15, January.
    7. Todic, Tamara & Stankovic, Vladimir & Stankovic, Lina, 2023. "An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem," Applied Energy, Elsevier, vol. 341(C).
    8. Pascal A. Schirmer & Iosif Mporas, 2019. "Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    9. İsmail Hakkı Çavdar & Vahit Feryad, 2021. "Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid," Energies, MDPI, vol. 14(15), pages 1-21, July.
    10. Muhammad Asif Ali Rehmani & Saad Aslam & Shafiqur Rahman Tito & Snjezana Soltic & Pieter Nieuwoudt & Neel Pandey & Mollah Daud Ahmed, 2021. "Power Profile and Thresholding Assisted Multi-Label NILM Classification," Energies, MDPI, vol. 14(22), pages 1-18, November.
    11. Feng, Yanxiao & Duan, Qiuhua & Chen, Xi & Yakkali, Sai Santosh & Wang, Julian, 2021. "Space cooling energy usage prediction based on utility data for residential buildings using machine learning methods," Applied Energy, Elsevier, vol. 291(C).
    12. Pascal A. Schirmer & Iosif Mporas & Akbar Sheikh-Akbari, 2020. "Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors," Energies, MDPI, vol. 13(9), pages 1-17, May.

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