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Cloud forecasting system for monitoring and alerting of energy use by home appliances

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  • Chou, Jui-Sheng
  • Truong, Ngoc-Son

Abstract

Inrecentyears,energy information systems have had an important role in the operational optimization of intelligent buildings to provide such benefits as high efficiency, energy savings and smart services. Interest in the intelligent management of home energy consumption using data mining and time series analysis is increasing. Therefore, this work develops an efficient web-based energy information management system for the power consumption of home appliances that monitors the energy load of a home, analyzes its energy consumption based on machine learning, and then sends information to various stakeholders. It interacts with the end-user through energy dashboards and emails. The web-based system includes a novel hybrid artificial intelligence model to improve its prediction of energy usage. An automatic warning function is also developed to identify anomalous energy consumption in a home in real time. The cloud system automatically sends a message to the user's email whenever a warning is necessary. End-users of this system can use forecast information and anomalous data to enhance the efficiency of energy usage in their buildings especially during peak times by adjusting the operating schedule of their appliances and electrical equipment.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:249:y:2019:i:c:p:166-177
    DOI: 10.1016/j.apenergy.2019.04.063
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    References listed on IDEAS

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    1. 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.
    2. Fróes Lima, Carlos Alberto & Portillo Navas, José Ricardo, 2012. "Smart metering and systems to support a conscious use of water and electricity," Energy, Elsevier, vol. 45(1), pages 528-540.
    3. Doostizadeh, Meysam & Ghasemi, Hassan, 2012. "A day-ahead electricity pricing model based on smart metering and demand-side management," Energy, Elsevier, vol. 46(1), pages 221-230.
    4. Malik, Arif S. & Bouzguenda, Mounir, 2013. "Effects of smart grid technologies on capacity and energy savings – A case study of Oman," Energy, Elsevier, vol. 54(C), pages 365-371.
    5. Usman, Ahmad & Shami, Sajjad Haider, 2013. "Evolution of Communication Technologies for Smart Grid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 191-199.
    6. Effenberger, Frank & Hilbert, Andreas, 2016. "Towards an energy information system architecture description for industrial manufacturers: Decomposition & allocation view," Energy, Elsevier, vol. 112(C), pages 599-605.
    7. Francisco, Abigail & Truong, Hanh & Khosrowpour, Ardalan & Taylor, John E. & Mohammadi, Neda, 2018. "Occupant perceptions of building information model-based energy visualizations in eco-feedback systems," Applied Energy, Elsevier, vol. 221(C), pages 220-228.
    8. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    9. Depuru, Soma Shekara Sreenadh Reddy & Wang, Lingfeng & Devabhaktuni, Vijay, 2011. "Smart meters for power grid: Challenges, issues, advantages and status," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2736-2742, August.
    10. 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.
    11. Li, Deng-Feng, 2011. "Linear programming approach to solve interval-valued matrix games," Omega, Elsevier, vol. 39(6), pages 655-666, December.
    12. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
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    3. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    4. Yamashita, Daniela Yassuda & Vechiu, Ionel & Gaubert, Jean-Paul, 2020. "A review of hierarchical control for building microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).

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