IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v193y2024ics1364032123010936.html
   My bibliography  Save this article

Multi-sensor data fusion framework for energy optimization in smart homes

Author

Listed:
  • Dasappa, Nirupam Sannagowdara
  • Kumar G, Krishna
  • Somu, Nivethitha

Abstract

Advancements in Internet of Energy (IoE) technologies drive the development of several energy efficient frameworks for better energy optimization, economic savings, safety, and security in smart homes. However, certain challenges such as real-time operational data for each micro-moment, proper application of data fusion techniques, and end-to-end computing and deployment architecture prevent the establishment of an effective energy-efficient framework to provide personalized energy-saving recommendations. This work presents energy management for smart spaces (EMSS), the proposed energy efficiency framework implemented in an edge-cloud computing platform that fuses data from heterogeneous data sources (environmental sensors, camera, plug data, etc.) at appropriate data fusion levels and process them to generate actionable, explainable, personalized, and persuasive recommendations at the right moment. The user response to the generated recommendations triggers the actuators to perform respective energy-saving actions and provide more personalized future recommendations. Further, SMARTHome - a data generation framework based on configurable scenario files and a set of software codes was proposed to generate synthetic data with respect to different building types and micro-moments. The functionalities of the EMSS (device and user registration), user dashboard, analytics, and energy-saving recommendations were made accessible to the user through web and mobile applications. The validation analysis of the EMSS was performed by (i) comparative analysis of the machine learning and deep learning algorithms used by the decision engine to generate energy-saving recommendations and (ii) benchmarking of EMSS based on the taxonomy of data fusion-based energy efficiency frameworks for smart homes.

Suggested Citation

  • Dasappa, Nirupam Sannagowdara & Kumar G, Krishna & Somu, Nivethitha, 2024. "Multi-sensor data fusion framework for energy optimization in smart homes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:rensus:v:193:y:2024:i:c:s1364032123010936
    DOI: 10.1016/j.rser.2023.114235
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032123010936
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2023.114235?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree," Applied Energy, Elsevier, vol. 267(C).
    2. Varlamis, Iraklis & Sardianos, Christos & Chronis, Christos & Dimitrakopoulos, George & Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2022. "Smart fusion of sensor data and human feedback for personalized energy-saving recommendations," Applied Energy, Elsevier, vol. 305(C).
    3. Valeriya Azarova & Dominik Engel & Cornelia Ferner & Andrea Kollmann & Johannes Reichl, 2018. "Exploring the impact of network tariffs on household electricity expenditures using load profiles and socio-economic characteristics," Nature Energy, Nature, vol. 3(4), pages 317-325, April.
    4. Isaac Machorro-Cano & Giner Alor-Hernández & Mario Andrés Paredes-Valverde & Lisbeth Rodríguez-Mazahua & José Luis Sánchez-Cervantes & José Oscar Olmedo-Aguirre, 2020. "HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving," Energies, MDPI, vol. 13(5), pages 1-24, March.
    5. Alsalemi, Abdullah & Ramadan, Mona & Bensaali, Faycal & Amira, Abbes & Sardianos, Christos & Varlamis, Iraklis & Dimitrakopoulos, George, 2019. "Endorsing domestic energy saving behavior using micro-moment classification," Applied Energy, Elsevier, vol. 250(C), pages 1302-1311.
    Full references (including those not matched with items on IDEAS)

    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. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).
    2. Josimar Reyes-Campos & Giner Alor-Hernández & Isaac Machorro-Cano & José Oscar Olmedo-Aguirre & José Luis Sánchez-Cervantes & Lisbeth Rodríguez-Mazahua, 2021. "Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm," Mathematics, MDPI, vol. 9(3), pages 1-25, January.
    3. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
    4. Pena-Bello, Alejandro & Junod, Robin & Ballif, Christophe & Wyrsch, Nicolas, 2023. "Balancing DSO interests and PV system economics with alternative tariffs," Energy Policy, Elsevier, vol. 183(C).
    5. Bovera, Filippo & Delfanti, Maurizio & Fumagalli, Elena & Lo Schiavo, Luca & Vailati, Riccardo, 2021. "Regulating electricity distribution networks under technological and demand uncertainty," Energy Policy, Elsevier, vol. 149(C).
    6. Eskander, Monica M. & Silva, Carlos A., 2023. "Techno-economic and environmental comparative analysis for DC microgrids in households: Portuguese and French household case study," Applied Energy, Elsevier, vol. 349(C).
    7. Himeur, Yassine & Ghanem, Khalida & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2021. "Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives," Applied Energy, Elsevier, vol. 287(C).
    8. Çimen, Halil & Bazmohammadi, Najmeh & Lashab, Abderezak & Terriche, Yacine & Vasquez, Juan C. & Guerrero, Josep M., 2022. "An online energy management system for AC/DC residential microgrids supported by non-intrusive load monitoring," Applied Energy, Elsevier, vol. 307(C).
    9. Vaughan, Jim & Doumen, Sjoerd C. & Kok, Koen, 2023. "Empowering tomorrow, controlling today: A multi-criteria assessment of distribution grid tariff designs," Applied Energy, Elsevier, vol. 341(C).
    10. Kong, Jun & Jiang, Wen & Tian, Qing & Jiang, Min & Liu, Tianshan, 2023. "Anomaly detection based on joint spatio-temporal learning for building electricity consumption," Applied Energy, Elsevier, vol. 334(C).
    11. Liu, Lucy & Workman, Mark & Hayes, Sarah, 2022. "Net Zero and the potential of consumer data - United Kingdom energy sector case study: The need for cross-sectoral best data practice principles," Energy Policy, Elsevier, vol. 163(C).
    12. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree," Applied Energy, Elsevier, vol. 267(C).
    13. Dodd, Tracey & Nelson, Tim, 2022. "Australian household adoption of solar photovoltaics: A comparative study of hardship and non-hardship customers," Energy Policy, Elsevier, vol. 160(C).
    14. Ansarin, Mohammad & Ghiassi-Farrokhfal, Yashar & Ketter, Wolfgang & Collins, John, 2020. "Cross-subsidies among residential electricity prosumers from tariff design and metering infrastructure," Energy Policy, Elsevier, vol. 145(C).
    15. Yuyang, Liu, 2024. "Natural resource efficiency and the road to a green economy: From scarcity to availability," Resources Policy, Elsevier, vol. 89(C).
    16. Hernández, José L. & de Miguel, Ignacio & Vélez, Fredy & Vasallo, Ali, 2024. "Challenges and opportunities in European smart buildings energy management: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    17. 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).
    18. Ansarin, Mohammad & Ghiassi-Farrokhfal, Yashar & Ketter, Wolfgang & Collins, John, 2020. "The economic consequences of electricity tariff design in a renewable energy era," Applied Energy, Elsevier, vol. 275(C).
    19. Tang, Wenjun & Wang, Hao & Lee, Xian-Long & Yang, Hong-Tzer, 2022. "Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data," Energy, Elsevier, vol. 240(C).
    20. Gunkel, Philipp Andreas & Bergaentzlé, Claire-Marie & Keles, Dogan & Scheller, Fabian & Jacobsen, Henrik Klinge, 2023. "Grid tariff designs to address electrification and their allocative impacts," Utilities Policy, Elsevier, vol. 85(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:eee:rensus:v:193:y:2024:i:c:s1364032123010936. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.