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المؤلفAtalla, Shadi
المؤلفHimeur, Yassine
المؤلفMansoor, Wathiq
المؤلفAmira, Abbes
المؤلفFadli, Fodil
المؤلفCopiaco, Abigail
المؤلفSohail, Shahab Saquib
تاريخ الإتاحة2024-03-18T09:46:06Z
تاريخ النشر2022-12
اسم المنشور2022 5th International Conference on Signal Processing and Information Security, ICSPIS 2022
المعرّفhttp://dx.doi.org/10.1109/ICSPIS57063.2022.10002437
الاقتباسAtalla, S., Himeur, Y., Mansoor, W., Amira, A., Fadli, F., Copiaco, A., & Sohail, S. S. (2022, December). Recommendation system towards residential energy saving based on anomaly detection. In 2022 5th International Conference on Signal Processing and Information Security (ICSPIS) (pp. 169-174). IEEE.
الترقيم الدولي الموحد للكتاب 978-166549265-2
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85147140140&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/53150
الملخصThis 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc. (IEEE)
الموضوعanomaly detection
EoT
machine learning
power consumption
Recommender
العنوانRecommendation System Towards Residential Energy Saving Based on Anomaly Detection
النوعConference Paper
الصفحات169-174
dc.accessType Full Text


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