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    Recommendation System Towards Residential Energy Saving Based on Anomaly Detection

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    Recommendation_System_Towards_Residential_Energy_Saving_Based_on_Anomaly_Detection.pdf (1.442Mb)
    Date
    2022-12
    Author
    Atalla, Shadi
    Himeur, Yassine
    Mansoor, Wathiq
    Amira, Abbes
    Fadli, Fodil
    Copiaco, Abigail
    Sohail, Shahab Saquib
    ...show more authors ...show less authors
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    Abstract
    This paper presents a recommender system to promote energy consumption reduction behaviors in residential buildings. The system exploits data stream processing methods jointly with machine learning algorithms on real-time residential data. Specifically, the data stream includes disaggregated power consumption, context, and weather conditions data. Internally the system converts time-series data streams into discrete ordered data points, which serve as inputs for training ML models. This method is used to predict power consumption anomalies. Gradually, the system helps to shape its users' activities into more energy-efficient ones. The experimental evaluation on real and simulated datasets demonstrates the promising performance of the proposed method, primarily when the K-neighbors neighbors' algorithm is used to classify the features extracted with interleaving current with the previous data points. The performance assessment of the machine learning algorithms shows the suitability of our implementation for Edge and Fog platforms in terms of accuracy, latency, and model size.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85147140140&origin=inward
    DOI/handle
    http://dx.doi.org/10.1109/ICSPIS57063.2022.10002437
    http://hdl.handle.net/10576/53150
    Collections
    • Architecture & Urban Planning [‎308‎ items ]

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