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Prediction of appliances energy use in smart homes

Author

Listed:
  • Arghira, Nicoleta
  • Hawarah, Lamis
  • Ploix, Stéphane
  • Jacomino, Mireille

Abstract

This paper presents methods for prediction of energy consumption of different appliances in homes. The aim is to predict the next day electricity consumption for some services in homes. Historical data for a set of homes in France was used. Two basic predictors are tested and a stochastic based predictor is proposed. The performance of the predictors is studied and it shows that the proposed predictor gives better results than other approaches. Two processings are proposed to improve the performance of the predictor, segmentation and aggregation of data. Application results are provided.

Suggested Citation

  • Arghira, Nicoleta & Hawarah, Lamis & Ploix, Stéphane & Jacomino, Mireille, 2012. "Prediction of appliances energy use in smart homes," Energy, Elsevier, vol. 48(1), pages 128-134.
    • Handle: RePEc:eee:energy:v:48:y:2012:i:1:p:128-134
    DOI: 10.1016/j.energy.2012.04.010
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    Cited by:

    1. Asfandyar Khan & Arif Iqbal Umar & Arslan Munir & Syed Hamad Shirazi & Muazzam A. Khan & Muhammad Adnan, 2021. "A QoS-Aware Machine Learning-Based Framework for AMI Applications in Smart Grids," Energies, MDPI, vol. 14(23), pages 1-22, December.
    2. Murray, D.M. & Liao, J. & Stankovic, L. & Stankovic, V., 2016. "Understanding usage patterns of electric kettle and energy saving potential," Applied Energy, Elsevier, vol. 171(C), pages 231-242.
    3. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    4. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
    5. Liu, Xiufeng & Nielsen, Per Sieverts, 2016. "A hybrid ICT-solution for smart meter data analytics," Energy, Elsevier, vol. 115(P3), pages 1710-1722.
    6. Premarathne, Uthpala Subodhani, 2015. "Reliable context-aware multi-attribute continuous authentication framework for secure energy utilization management in smart homes," Energy, Elsevier, vol. 93(P1), pages 1210-1221.
    7. Seok-Jun Bu & Sung-Bae Cho, 2020. "Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption," Energies, MDPI, vol. 13(18), pages 1-16, September.
    8. Hong, Seung Ho & Yu, Mengmeng & Huang, Xuefei, 2015. "A real-time demand response algorithm for heterogeneous devices in buildings and homes," Energy, Elsevier, vol. 80(C), pages 123-132.
    9. 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.
    10. Chou, Jui-Sheng & Truong, Ngoc-Son, 2019. "Cloud forecasting system for monitoring and alerting of energy use by home appliances," Applied Energy, Elsevier, vol. 249(C), pages 166-177.
    11. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.
    12. Khemakhem, Siwar & Rekik, Mouna & Krichen, Lotfi, 2017. "A flexible control strategy of plug-in electric vehicles operating in seven modes for smoothing load power curves in smart grid," Energy, Elsevier, vol. 118(C), pages 197-208.
    13. López-Rodríguez, M.A. & Santiago, I. & Trillo-Montero, D. & Torriti, J. & Moreno-Munoz, A., 2013. "Analysis and modeling of active occupancy of the residential sector in Spain: An indicator of residential electricity consumption," Energy Policy, Elsevier, vol. 62(C), pages 742-751.
    14. Muratori, Matteo & Roberts, Matthew C. & Sioshansi, Ramteen & Marano, Vincenzo & Rizzoni, Giorgio, 2013. "A highly resolved modeling technique to simulate residential power demand," Applied Energy, Elsevier, vol. 107(C), pages 465-473.
    15. Choi, Dae-Hyun & Xie, Le, 2016. "A framework for sensitivity analysis of data errors on home energy management system," Energy, Elsevier, vol. 117(P1), pages 166-175.
    16. 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.
    17. Palacios-Garcia, E.J. & Moreno-Munoz, A. & Santiago, I. & Flores-Arias, J.M. & Bellido-Outeirino, F.J. & Moreno-Garcia, I.M., 2018. "A stochastic modelling and simulation approach to heating and cooling electricity consumption in the residential sector," Energy, Elsevier, vol. 144(C), pages 1080-1091.
    18. Ebrahimzadeh Sarvestani, Maryam & Hoseiny, Saeed & Tavana, Davood & Di Maria, Francesco, 2024. "Strategic management of energy consumption and reduction of specific energy consumption using modern methods of artificial intelligence in an industrial plant," Energy, Elsevier, vol. 286(C).
    19. Iulia Stamatescu & Nicoleta Arghira & Ioana Făgărăşan & Grigore Stamatescu & Sergiu Stelian Iliescu & Vasile Calofir, 2017. "Decision Support System for a Low Voltage Renewable Energy System," Energies, MDPI, vol. 10(1), pages 1-15, January.
    20. Balta-Ozkan, Nazmiye & Davidson, Rosemary & Bicket, Martha & Whitmarsh, Lorraine, 2013. "The development of smart homes market in the UK," Energy, Elsevier, vol. 60(C), pages 361-372.

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