Abstract
This paper aims to study energy consumption in a house. Home energy management system (HEMS) has become very important, because energy consumption of a residential sector accounts for a significant amount of total energy consumption. However, a conventional HEMS has some architectural limitations among dimensional variables reusability and interoperability. Furthermore, the cost of implementation in HEMS is very expensive, which leads to the disturbance of the spread of a HEMS. Therefore, this study proposes an Internet of Things (IoT) based HEMS with lightweight photovoltaic (PV) system over dynamic home area networks (DHANs), which enables the construction of a HEMS to be scalable reusable and interoperable. The study suggests a technique for decreasing cost of energy that HEMS is using and various perspectives in system. The method that proposed is K-NN (K-Nearest Neighbor) which helps us to analyze the classification and regression datasets. This paper has the result from the data relevant in October 2018 from some buildings of Nanjing University of Information Science and Technology. That dataset allowed us to make analysis of electric energy consumption of each home equipment used and to make a simulation of the energy needed for each apparatus. Finally, we succeeded to find the algorithm which is suitable for efficiency-electric energy.
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Acknowledgement
This work has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 701697, Major Program of the National Social Science Fund of China (Grant No. 17ZDA092), Basic Research Programs (Natural Science Foundation) of Jiangsu Province (BK20180794), 333 High-Level Talent Cultivation Project of Jiangsu Province (BRA2018332) and the PAPD fund.
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Sun, M., Liu, X., Mbonihankuye, S. (2019). Analysis of the Efficiency-Energy with Regression and Classification in Household Using K-NN. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_31
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