IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i9p2195-d353213.html
   My bibliography  Save this article

A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing

Author

Listed:
  • Hasan Rafiq

    (Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250061, China)

  • Xiaohan Shi

    (Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250061, China)

  • Hengxu Zhang

    (Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250061, China)

  • Huimin Li

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China)

  • Manesh Kumar Ochani

    (Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250061, China)

Abstract

Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and post-processing. First, the mutual information method was used to select electrical parameters that had the most influence on the power consumption of each target appliance. Second, selected steady-state parameters based multi-feature input space (MFS) was used to train the 4-layered bidirectional long short-term memory (LSTM) model for each target appliance. Finally, a post-processing technique was used at the disaggregation stage to eliminate irrelevant predicted sequences, enhancing the classification and estimation accuracy of the algorithm. A comprehensive evaluation was conducted on 1-Hz sampled UKDALE and ECO datasets in a noised scenario with seen and unseen test cases. Performance evaluation showed that the MFS-LSTM algorithm is computationally efficient, scalable, and possesses better estimation accuracy in a noised scenario, and generalized to unseen loads as compared to benchmark algorithms. Presented results proved that the proposed algorithm fulfills practical application requirements and can be deployed in real-time.

Suggested Citation

  • Hasan Rafiq & Xiaohan Shi & Hengxu Zhang & Huimin Li & Manesh Kumar Ochani, 2020. "A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing," Energies, MDPI, vol. 13(9), pages 1-26, May.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2195-:d:353213
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/9/2195/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/9/2195/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manoj Manivannan & Behzad Najafi & Fabio Rinaldi, 2017. "Machine Learning-Based Short-Term Prediction of Air-Conditioning Load through Smart Meter Analytics," Energies, MDPI, vol. 10(11), pages 1-17, November.
    2. 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.
    3. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    4. Ying Zhang & Bo Yin & Yanping Cong & Zehua Du, 2020. "Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering," Energies, MDPI, vol. 13(4), pages 1-12, February.
    5. Esa, Nur Farahin & Abdullah, Md Pauzi & Hassan, Mohammad Yusri, 2016. "A review disaggregation method in Non-intrusive Appliance Load Monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 163-173.
    6. Hsueh-Hsien Chang, 2012. "Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses," Energies, MDPI, vol. 5(11), pages 1-21, November.
    7. Paula Meehan & Conor McArdle & Stephen Daniels, 2014. "An Efficient, Scalable Time-Frequency Method for Tracking Energy Usage of Domestic Appliances Using a Two-Step Classification Algorithm," Energies, MDPI, vol. 7(11), pages 1-26, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Xia, Yingqi & Sun, Gengchen & Wang, Yanfeng & Yang, Qing & Wang, Qingrui & Ba, Shusong, 2024. "A novel carbon emission estimation method based on electricity‑carbon nexus and non-intrusive load monitoring," Applied Energy, Elsevier, vol. 360(C).
    3. 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.
    4. 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.
    5. 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).
    6. Netzah Calamaro & Moshe Donko & Doron Shmilovitz, 2021. "A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements," Energies, MDPI, vol. 14(21), pages 1-37, November.
    7. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Antonio Ruano & Alvaro Hernandez & Jesus Ureña & Maria Ruano & Juan Garcia, 2019. "NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review," Energies, MDPI, vol. 12(11), pages 1-29, June.
    2. Liu, Bo & Luan, Wenpeng & Yu, Yixin, 2017. "Dynamic time warping based non-intrusive load transient identification," Applied Energy, Elsevier, vol. 195(C), pages 634-645.
    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. Anwar Ul Haq & Hans-Arno Jacobsen, 2018. "Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review," Energies, MDPI, vol. 11(1), pages 1-22, January.
    5. Enríquez, R. & Jiménez, M.J. & Heras, M.R., 2017. "Towards non-intrusive thermal load Monitoring of buildings: BES calibration," Applied Energy, Elsevier, vol. 191(C), pages 44-54.
    6. Darío Baptista & Sheikh Shanawaz Mostafa & Lucas Pereira & Leonel Sousa & Fernando Morgado-Dias, 2018. "Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory," Energies, MDPI, vol. 11(9), pages 1-18, September.
    7. Sara Tavakoli & Kaveh Khalilpour, 2021. "A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability," Energies, MDPI, vol. 14(16), pages 1-27, August.
    8. Benjamin Völker & Andreas Reinhardt & Anthony Faustine & Lucas Pereira, 2021. "Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective," Energies, MDPI, vol. 14(3), pages 1-21, January.
    9. Bonfigli, Roberto & Principi, Emanuele & Fagiani, Marco & Severini, Marco & Squartini, Stefano & Piazza, Francesco, 2017. "Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models," Applied Energy, Elsevier, vol. 208(C), pages 1590-1607.
    10. Mingzhe Zou & Shuyang Zhu & Jiacheng Gu & Lidija M. Korunovic & Sasa Z. Djokic, 2021. "Heating and Lighting Load Disaggregation Using Frequency Components and Convolutional Bidirectional Long Short-Term Memory Method," Energies, MDPI, vol. 14(16), pages 1-24, August.
    11. Esa, Nur Farahin & Abdullah, Md Pauzi & Hassan, Mohammad Yusri, 2016. "A review disaggregation method in Non-intrusive Appliance Load Monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 163-173.
    12. Shimoda, Yoshiyuki & Yamaguchi, Yohei & Iwafune, Yumiko & Hidaka, Kazuyoshi & Meier, Alan & Yagita, Yoshie & Kawamoto, Hisaki & Nishikiori, Soichi, 2020. "Energy demand science for a decarbonized society in the context of the residential sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    13. Augustyn Wójcik & Piotr Bilski & Robert Łukaszewski & Krzysztof Dowalla & Ryszard Kowalik, 2021. "Identification of the State of Electrical Appliances with the Use of a Pulse Signal Generator," Energies, MDPI, vol. 14(3), pages 1-26, January.
    14. Wu, Junqi & Niu, Zhibin & Li, Xiang & Huang, Lizhen & Nielsen, Per Sieverts & Liu, Xiufeng, 2023. "Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach," Energy, Elsevier, vol. 263(PD).
    15. Katarzyna Stasiuk & Dominika Maison, 2022. "The Influence of New and Old Energy Labels on Consumer Judgements and Decisions about Household Appliances," Energies, MDPI, vol. 15(4), pages 1-13, February.
    16. Chatzigeorgiou, I.M. & Andreou, G.T., 2021. "A systematic review on feedback research for residential energy behavior change through mobile and web interfaces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    17. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    18. 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.
    19. Zhou, Yang & Shi, Zhixiong & Shi, Zhengyu & Gao, Qing & Wu, Libo, 2019. "Disaggregating power consumption of commercial buildings based on the finite mixture model," Applied Energy, Elsevier, vol. 243(C), pages 35-46.
    20. Lang, Corey & Okwelum, Edson, 2015. "The mitigating effect of strategic behavior on the net benefits of a direct load control program," Energy Economics, Elsevier, vol. 49(C), pages 141-148.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2195-:d:353213. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.