Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/379
Title: | Smart-Building Applications: Deep Learning-Based, Real-Time Load Monitoring | Authors: | Çimen, Halil Palacios-Garcia, Emilio J. Kolbaek, Morten Çetinkaya, Nurettin Vasquez, Juan C. Guerrero, Josep M. |
Keywords: | Home appliances Data models Monitoring Feature extraction Smart meters Internet of Things Hidden Markov models Algorithm design and theory SYSTEM |
Issue Date: | 2021 | Publisher: | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Abstract: | Google's Director of Research Peter Norvig said, We don't have better algorithms than anyone else; we just have more data. This inspiring statement shows that having more data is directly related to better decision making and foresight about the future. With the development of Internet of Things (IoT) technology, it is now much easier to gather data. Technological tools, such as social media websites, smartphones, and security cameras, can be considered as data generators. When the focus is shifted to the energy field, these generators are smart meters. | URI: | https://doi.org/10.1109/MIE.2020.3023075 https://hdl.handle.net/20.500.13091/379 |
ISSN: | 1932-4529 1941-0115 |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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Smart-Building_Applications_Deep_Learning-Based_Real-Time_Load_Monitoring.pdf Until 2030-01-01 | 1.67 MB | Adobe PDF | View/Open Request a copy |
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