This paper aims to study energy consumption in a house. Home energy managementsystem (HEMS) has become very important, because energy consumption of aresidential sector accounts for a significant amount of total energ...This paper aims to study energy consumption in a house. Home energy managementsystem (HEMS) has become very important, because energy consumption of aresidential sector accounts for a significant amount of total energy consumption.However, a conventional HEMS has some architectural limitations among dimensionalvariables reusability and interoperability. Furthermore, the cost of implementation inHEMS 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 lightweightphotovoltaic (PV) system over dynamic home area networks (DHANs), which enablesthe construction of a HEMS to be scalable reusable and interoperable. The study suggestsa technique for decreasing cost of energy that HEMS is using and various perspectives insystem. The method that proposed is K-NN (K-Nearest Neighbor) which helps us toanalyze the classification and regression datasets. This paper has the result from the datarelevant in October 2018 from some buildings of Nanjing University of InformationScience and Technology.展开更多
文摘This paper aims to study energy consumption in a house. Home energy managementsystem (HEMS) has become very important, because energy consumption of aresidential sector accounts for a significant amount of total energy consumption.However, a conventional HEMS has some architectural limitations among dimensionalvariables reusability and interoperability. Furthermore, the cost of implementation inHEMS 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 lightweightphotovoltaic (PV) system over dynamic home area networks (DHANs), which enablesthe construction of a HEMS to be scalable reusable and interoperable. The study suggestsa technique for decreasing cost of energy that HEMS is using and various perspectives insystem. The method that proposed is K-NN (K-Nearest Neighbor) which helps us toanalyze the classification and regression datasets. This paper has the result from the datarelevant in October 2018 from some buildings of Nanjing University of InformationScience and Technology.
