Analyzing the Energy Consumption of Smart Home and Price Prediction
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This paper describes a system that is used to gather data from Smart Home sensors measuring energy consumption, analyze and predict future consumption by using machine learning algorithm for prediction. It focuses on the architecture of the system as well as the need for such system to exist in practice – why the consumption should be measured at all and how that measurement can be put to good use for achieving energy efficiency.
D. Mohamed, D. El-Menshawy, S. El-Abd, M. Ahmad, and M. Samir Abou El-Seoud, “I energy efficiency in cloud computing,” International Journal of Machine Learning and Computing, vol. 9, no. 1, 2019.
Lin, Yu-Hsiu & Tang, Huei-Sheng & Shen, Ting-Yu & Hsia, Chih-Hsien. (2022). A Smart Home Energy Management System Utilizing Neurocomputing-Based Time-Series Load Modeling and Forecasting Facilitated by Energy Decomposition for Smart Home Automation. IEEE Access. PP. 1-1.
1109/ACCESS.2022.3219068.
B. Völker, A. Reinhardt, A. Faustine, and L. Pereira, “Watt’s up at home? Smart meter data analytics from a consumer-centric perspective,” Energies, vol. 14, p. 719, 2021.
A. Tundis, A. Faizan, and M. Mühlhäuser, “A feature-based model for the identification of electrical devices in smart environments,” Sensors, vol. 19, p. 2611, 2019.
Y. Himeur, A. Alsalemi, F. Bensaali, A. Amira, and A. Al-Kababji, “Recent trends of smart non-intrusive load monitoring in buildings: a review, open challenges and future directions,” Int. J. Intell. Syst., Early Access, pp. 1-56, 2022.
E. McKenna, I. Richardson, and M. Thomson, “Smart meter data: Balancing consumer privacy concerns with legitimate applications,” Energy Policy, vol. 41, pp. 807-814, 2012.
L. G. Fahad and S. F. Tahir, “Activity recognition and anomaly detection in smart homes,” Neurocomputing, vol. 423, pp. 362-372, 2021.
Y. H. Lin, “An advanced smart home energy management system considering identification of ADLs based on non-intrusive load monitoring,” Electr. Eng., Early Access, 2022.
M. Amayri, S. Ploix, H. Kazmi, Q. D. Ngo, and E. A. E. Safadi, “Estimating occupancy from measurements and knowledge using the Bayesian network for energy management,” J. Sensors, vol. 2019, p. 7129872, 2019.
Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr, Wei-Sheng Chin, Yael Dekel, Xavier Dupre, Vadim Eksarevskiy, Senja Filipi, Tom Finley, Abhishek Goswami, Monte Hoover, Scott Inglis, Matteo Interlandi, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, and Yiwen Zhu. 2019. Machine Learning at Microsoft with ML.NET. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19). Association for Computing Machinery, New York, NY, USA, 2448–2458.
https://doi.org/10.1145/3292500.3330667
Xinlong Bao. 2009. Applying machine learning for prediction, recommendation, and integration. Ph.D. Dissertation. Oregon State University, USA. Advisor(s) Tom Dietterich. Order Number: AAI3380852.