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Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses

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  • Hsueh-Hsien Chang

    (Department of Electronic Engineering, Jin-Wen University of Science and Technology, New Taipei 23154, Taiwan)

Abstract

Energy management systems strive to use energy resources efficiently, save energy, and reduce carbon output. This study proposes transient feature analyses of the transient response time and transient energy on the power signatures of non-intrusive demand monitoring and load identification to detect the power demand and load operation. This study uses the wavelet transform (WT) of the time-frequency domain to analyze and detect the transient physical behavior of loads during the load identification. The experimental results show the transient response time and transient energy are better than the steady-state features to improve the recognition accuracy and reduces computation requirements in non-intrusive load monitoring (NILM) systems. The discrete wavelet transform (DWT) is more suitable than short-time Fourier transform (STFT) for transient load analyses.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:11:p:4569-4589:d:21447
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    References listed on IDEAS

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    1. Ying-Yi Hong & Jing-Han Chou, 2012. "Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks," Energies, MDPI, vol. 5(7), pages 1-16, July.
    2. Chang, Hsueh-Hsien, 2011. "Genetic algorithms and non-intrusive energy management system based economic dispatch for cogeneration units," Energy, Elsevier, vol. 36(1), pages 181-190.
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    Cited by:

    1. Younghoon Kwak & Jihyun Hwang & Taewon Lee, 2018. "Load Disaggregation via Pattern Recognition: A Feasibility Study of a Novel Method in Residential Building," Energies, MDPI, vol. 11(4), pages 1-22, April.
    2. Wesley Angelino de Souza & Fernando Deluno Garcia & Fernando Pinhabel Marafão & Luiz Carlos Pereira da Silva & Marcelo Godoy Simões, 2019. "Load Disaggregation Using Microscopic Power Features and Pattern Recognition," Energies, MDPI, vol. 12(14), pages 1-18, July.
    3. 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.
    4. André Eugenio Lazzaretti & Douglas Paulo Bertrand Renaux & Carlos Raimundo Erig Lima & Bruna Machado Mulinari & Hellen Cristina Ancelmo & Elder Oroski & Fabiana Pöttker & Robson Ribeiro Linhares & Luc, 2020. "A Multi-Agent NILM Architecture for Event Detection and Load Classification," Energies, MDPI, vol. 13(17), pages 1-35, August.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Wang, Gang & Li, Zhao & Luo, Zhao & Zhang, Tao & Lin, Mingliang & Li, Jiahao & Shen, Xin, 2024. "Dynamic adaptive event detection strategy based on power change-point weighting model," Applied Energy, Elsevier, vol. 361(C).
    11. 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.
    12. Hsueh-Hsien Chang & Nguyen Viet Linh, 2017. "Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems," Energies, MDPI, vol. 10(5), pages 1-20, April.
    13. Guopeng Song & Hao Chen & Bo Guo, 2014. "A Layered Fault Tree Model for Reliability Evaluation of Smart Grids," Energies, MDPI, vol. 7(8), pages 1-23, July.
    14. 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.
    15. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.
    16. 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.
    17. Soon-Ryul Nam & Sang-Hee Kang & Joo-Ho Lee & Eun-Jae Choi & Seon-Ju Ahn & Joon-Ho Choi, 2013. "EMS-Data-Based Load Modeling to Evaluate the Effect of Conservation Voltage Reduction at a National Level," Energies, MDPI, vol. 6(8), pages 1-14, July.
    18. Lefeng Cheng & Zhiyi Zhang & Haorong Jiang & Tao Yu & Wenrui Wang & Weifeng Xu & Jinxiu Hua, 2018. "Local Energy Management and Optimization: A Novel Energy Universal Service Bus System Based on Energy Internet Technologies," Energies, MDPI, vol. 11(5), pages 1-38, May.
    19. Aggelos S. Bouhouras & Paschalis A. Gkaidatzis & Konstantinos C. Chatzisavvas & Evangelos Panagiotou & Nikolaos Poulakis & Georgios C. Christoforidis, 2017. "Load Signature Formulation for Non-Intrusive Load Monitoring Based on Current Measurements," Energies, MDPI, vol. 10(4), pages 1-21, April.
    20. Krzysztof Gajowniczek & Tomasz Ząbkowski, 2015. "Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data," Energies, MDPI, vol. 8(7), pages 1-21, July.
    21. 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.

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