IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i1p188-d473649.html
   My bibliography  Save this article

The Impact of Ambient Sensing on the Recognition of Electrical Appliances

Author

Listed:
  • Jana Huchtkoetter

    (Department of Informatics, TU Clausthal, 38678 Clausthal-Zellerfeld, Germany)

  • Marcel Alwin Tepe

    (Department of Informatics, TU Clausthal, 38678 Clausthal-Zellerfeld, Germany)

  • Andreas Reinhardt

    (Department of Informatics, TU Clausthal, 38678 Clausthal-Zellerfeld, Germany)

Abstract

Smart spaces are characterized by their ability to capture a holistic picture of their contextual situation. This often includes the detection of the operative states of electrical appliances, which in turn allows for the recognition of user activities and intentions. For electrical appliances with largely different power consumption characteristics, their types and operational times can be easily inferred from data collected at a single metering point (typically, a smart meter). However, a disambiguation between consumers of the same type and model, yet located in different areas of a smart building, is not possible this way. Likewise, small consumers (e.g., wall chargers) are often indiscernible from measurement noise and spurious power consumption events of other appliances. As a consequence thereof, we investigate how additional sensing modalities, i.e., data beyond electrical signals, can be leveraged to improve the appliance detection accuracy. Through a set of practical experiments, recording ambient influences in eight dimensions and testing their effects on 21 appliance types, we evaluate the importance of such added features in the context of appliance recognition. Our results show that electrical power measurements already yield a high appliance recognition accuracy, yet further accuracy improvements are possible when considering ambient parameters as well.

Suggested Citation

  • Jana Huchtkoetter & Marcel Alwin Tepe & Andreas Reinhardt, 2021. "The Impact of Ambient Sensing on the Recognition of Electrical Appliances," Energies, MDPI, vol. 14(1), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:1:p:188-:d:473649
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/1/188/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/1/188/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Cabeza, Luisa F. & Ürge-Vorsatz, Diana & Palacios, Anabel & Ürge, Daniel & Serrano, Susana & Barreneche, Camila, 2018. "Trends in penetration and ownership of household appliances," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 4044-4059.
    3. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
    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. Patricia Franco & José M. Martínez & Young-Chon Kim & Mohamed A. Ahmed, 2022. "A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions," Sustainability, MDPI, vol. 14(8), pages 1-33, April.
    2. Andreas Reinhardt & Lucas Pereira, 2021. "Special Issue: “Energy Data Analytics for Smart Meter Data”," Energies, MDPI, vol. 14(17), pages 1-3, August.

    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. 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.
    2. Frankel, Matthew & Xing, Lu & Chewning, Connor & Sela, Lina, 2021. "Water-energy benchmarking and predictive modeling in multi-family residential and non-residential buildings," Applied Energy, Elsevier, vol. 281(C).
    3. Andreas Dellnitz & Andreas Kleine & Madjid Tavana, 2024. "An integrated data envelopment analysis and regression tree method for new product price estimation," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(4), pages 1189-1211, December.
    4. 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).
    5. 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).
    6. Ariana Chang & Tian‐Shyug Lee & Hsiu‐Mei Lee, 2024. "Applying sustainable development goals in financial forecasting using machine learning techniques," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(3), pages 2277-2289, May.
    7. 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.
    8. 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.
    9. 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.
    10. Coelho, Igor M. & Coelho, Vitor N. & Luz, Eduardo J. da S. & Ochi, Luiz S. & Guimarães, Frederico G. & Rios, Eyder, 2017. "A GPU deep learning metaheuristic based model for time series forecasting," Applied Energy, Elsevier, vol. 201(C), pages 412-418.
    11. Farkas, Sébastien & Lopez, Olivier & Thomas, Maud, 2021. "Cyber claim analysis using Generalized Pareto regression trees with applications to insurance," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 92-105.
    12. Iana Vassileva & Javier Campillo, 2016. "Consumers’ Perspective on Full-Scale Adoption of Smart Meters: A Case Study in Västerås, Sweden," Resources, MDPI, vol. 5(1), pages 1-18, January.
    13. Schultz, P. Wesley & Estrada, Mica & Schmitt, Joseph & Sokoloski, Rebecca & Silva-Send, Nilmini, 2015. "Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms," Energy, Elsevier, vol. 90(P1), pages 351-358.
    14. Benjamin Völker & Marc Pfeifer & Philipp M. Scholl & Bernd Becker, 2020. "A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring," Energies, MDPI, vol. 14(1), pages 1-26, December.
    15. Emilio Aguirre & Federico García-Suárez & Gabriela Sicilia, 2021. "Eficiencia técnica en la ganadería de carne bovina pastoril. Medición y exploración de sus determinantes en Uruguay," Documentos de Trabajo (working papers) 1321, Department of Economics - dECON.
    16. Astier, Nicolas, 2018. "Comparative feedbacks under incomplete information," Resource and Energy Economics, Elsevier, vol. 54(C), pages 90-108.
    17. 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.
    18. Lotfi Boudabsa & Damir Filipovi'c, 2022. "Ensemble learning for portfolio valuation and risk management," Papers 2204.05926, arXiv.org.
    19. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    20. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).

    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:14:y:2021:i:1:p:188-:d:473649. 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.