IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i23p9073-d989145.html
   My bibliography  Save this article

Energy Disaggregation Using Multi-Objective Genetic Algorithm Designed Neural Networks

Author

Listed:
  • Inoussa Laouali

    (DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    SIGER, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez P.O. Box 2202, Morocco)

  • Isaías Gomes

    (DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1950-044 Lisboa, Portugal
    ICT, University of Evora, 7002-554 Evora, Portugal)

  • Maria da Graça Ruano

    (DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    CISUC, Faculty of Science & Technology, University of Coimbra, 3030-290 Coimbra, Portugal)

  • Saad Dosse Bennani

    (SIGER, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez P.O. Box 2202, Morocco)

  • Hakim El Fadili

    (LIPI, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Bensouda, Fez P.O. Box 5206, Morocco)

  • Antonio Ruano

    (DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1950-044 Lisboa, Portugal)

Abstract

Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices.

Suggested Citation

  • Inoussa Laouali & Isaías Gomes & Maria da Graça Ruano & Saad Dosse Bennani & Hakim El Fadili & Antonio Ruano, 2022. "Energy Disaggregation Using Multi-Objective Genetic Algorithm Designed Neural Networks," Energies, MDPI, vol. 15(23), pages 1-29, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9073-:d:989145
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/23/9073/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/23/9073/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
    2. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
    3. 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.
    4. Balezentis, Tomas, 2020. "Shrinking ageing population and other drivers of energy consumption and CO2 emission in the residential sector: A case from Eastern Europe," Energy Policy, Elsevier, vol. 140(C).
    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. Bartłomiej Gawin & Robert Małkowski & Robert Rink, 2023. "Will NILM Technology Replace Multi-Meter Telemetry Systems for Monitoring Electricity Consumption?," Energies, MDPI, vol. 16(5), pages 1-26, February.

    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. 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.
    2. Kim, Minsoo & Park, Taeseop & Jeong, Jaeik & Kim, Hongseok, 2023. "Stochastic optimization of home energy management system using clustered quantile scenario reduction," Applied Energy, Elsevier, vol. 349(C).
    3. Abudureheman, Maliyamu & Jiang, Qingzhe & Dong, Xiucheng & Dong, Cong, 2022. "Spatial effects of dynamic comprehensive energy efficiency on CO2 reduction in China," Energy Policy, Elsevier, vol. 166(C).
    4. 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.
    5. Feng Dong & Yuling Pan, 2020. "Evolution of Renewable Energy in BRI Countries: A Combined Econometric and Decomposition Approach," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    6. Cristina Puente & Rafael Palacios & Yolanda González-Arechavala & Eugenio Francisco Sánchez-Úbeda, 2020. "Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques," Energies, MDPI, vol. 13(12), pages 1-20, June.
    7. Yu, Zhang & Khan, Syed Abdul Rehman & Ponce, Pablo & Lopes de Sousa Jabbour, Ana Beatriz & Chiappetta Jabbour, Charbel Jose, 2022. "Factors affecting carbon emissions in emerging economies in the context of a green recovery: Implications for sustainable development goals," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    8. Zhe Zhao & Xin Xuan & Fan Zhang & Ying Cai & Xiaoyu Wang, 2022. "Scenario Analysis of Renewable Energy Development and Carbon Emission in the Beijing–Tianjin–Hebei Region," Land, MDPI, vol. 11(10), pages 1-13, September.
    9. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    10. Dewangan, Chaman Lal & Vijayan, Vineeth & Shukla, Devesh & Chakrabarti, S. & Singh, S.N. & Sharma, Ankush & Hossain, Md. Alamgir, 2023. "An improved decentralized scheme for incentive-based demand response from residential customers," Energy, Elsevier, vol. 284(C).
    11. Veronica Piccialli & Antonio M. Sudoso, 2021. "Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network," Energies, MDPI, vol. 14(4), pages 1-16, February.
    12. Everton Luiz de Aguiar & André Eugenio Lazzaretti & Bruna Machado Mulinari & Daniel Rodrigues Pipa, 2021. "Scattering Transform for Classification in Non-Intrusive Load Monitoring," Energies, MDPI, vol. 14(20), pages 1-20, October.
    13. Yichao Xie & Bowen Zhou & Zhenyu Wang & Bo Yang & Liaoyi Ning & Yanhui Zhang, 2023. "Industrial Carbon Footprint (ICF) Calculation Approach Based on Bayesian Cross-Validation Improved Cyclic Stacking," Sustainability, MDPI, vol. 15(19), pages 1-35, September.
    14. Yang, Chao & Liang, Gaoqi & Liu, Jinjie & Liu, Guolong & Yang, Hongming & Zhao, Junhua & Dong, Zhaoyang, 2023. "A non-intrusive carbon emission accounting method for industrial corporations from the perspective of modern power systems," Applied Energy, Elsevier, vol. 350(C).
    15. Škare, Marinko & Porada-Rochoń, Małgorzata, 2023. "Are we making progress on decarbonization? A panel heterogeneous study of the long-run relationship in selected economies," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    16. Javier Parra-Domínguez & Esteban Sánchez & Ángel Ordóñez, 2023. "The Prosumer: A Systematic Review of the New Paradigm in Energy and Sustainable Development," Sustainability, MDPI, vol. 15(13), pages 1-44, July.
    17. Jiyong Park & Taeyoung Jin & Sungin Lee & Jongroul Woo, 2021. "Industrial Electrification and Efficiency: Decomposition Evidence from the Korean Industrial Sector," Energies, MDPI, vol. 14(16), pages 1-18, August.
    18. Xi He & Heng Dong & Wanli Yang & Jun Hong, 2022. "A Novel Denoising Auto-Encoder-Based Approach for Non-Intrusive Residential Load Monitoring," Energies, MDPI, vol. 15(6), pages 1-15, March.
    19. Xiao-Yu Zhang & Stefanie Kuenzel & José-Rodrigo Córdoba-Pachón & Chris Watkins, 2020. "Privacy-Functionality Trade-Off: A Privacy-Preserving Multi-Channel Smart Metering System," Energies, MDPI, vol. 13(12), pages 1-30, June.
    20. Hari Prasad Devarapalli & Venkata Samba Sesha Siva Sarma Dhanikonda & Sitarama Brahmam Gunturi, 2021. "Demand-Side Management for Improvement of the Power Quality in Smart Homes Using Non-Intrusive Identification of Appliance Usage Patterns with the True Power Factor," Energies, MDPI, vol. 14(16), pages 1-19, August.

    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:15:y:2022:i:23:p:9073-:d:989145. 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.