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Analysis of the Efficiency-Energy with Regression and Classification in Household Using K-NN

Qi Liu1,2, Zhiyun Yang1, Xiaodong Liu3, Scholas Mbonihankuye4,*

School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Shandong Beiming Medical Technology Ltd., 777 Shunfeng Rd, Jinan, 250101, China.
School of Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, UK.
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, 210044, China.

*Corresponding Author: Scholas Mbonihankuye. Email: .

Journal of New Media 2019, 1(2), 101-113.


This paper aims to study energy consumption in a house. Home energy man-agement 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.


Cite This Article

Q. Liu, Z. Yang, X. Liu and S. Mbonihankuye, "Analysis of the efficiency-energy with regression and classification in household using k-nn," Journal of New Media, vol. 1, no.2, pp. 101–113, 2019.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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