scholarly journals Mobile Web Energy Monitoring System Using DFRduino Uno

2017 ◽  
Vol 124 ◽  
pp. 706-713 ◽  
Author(s):  
Kristine Mae E. Galera ◽  
Orven E. Llantos
Keyword(s):  
Monitoring System ◽  
Energy Monitoring ◽  
Mobile Web

A Critical Study on Campus Energy Monitoring System and Role of IoT

2021 ◽  
Author(s):  
Mopuri Deepika ◽  
Merugu Kavitha ◽  
N. S. Kalyan Chakravarthy ◽  
J. Srinivas Rao ◽  
D. Mohan Reddy ◽  
...  
Keyword(s):  
Monitoring System ◽  
Critical Study ◽  
Energy Monitoring ◽  
Campus Energy

Semi-supervised Intrusive Appliance Load Monitoring in Smart Energy Monitoring System

2021 ◽  
Vol 17 (3) ◽  
pp. 1-20
Author(s):  
Vanh Khuyen Nguyen ◽  
Wei Emma Zhang ◽  
Adnan Mahmood
Keyword(s):  
Supervised Learning ◽  
Monitoring System ◽  
End Users ◽  
Training Data ◽  
Smart Device ◽  
Energy Monitoring ◽  
Load Monitoring ◽  
Benchmark Datasets ◽  
Smart Home Environment ◽  
Smart Appliance

Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The samples fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method’s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.

Development of an energy monitoring system for large public buildings

2013 ◽  
Vol 66 ◽  
pp. 41-48 ◽  
Author(s):  
Liang Zhao ◽  
Ji-li Zhang ◽  
Ruo-bing Liang
Keyword(s):  
Monitoring System ◽  
Public Buildings ◽  
Energy Monitoring

Design of Electric Energy Monitoring System Based on MAXQ3180 Chip

2014 ◽  
Vol 521 ◽  
pp. 435-439
Author(s):  
Cheng Hao Han ◽  
Xiang Tong Wang ◽  
Hao Li ◽  
Ying Qin ◽  
Dong Yu Liu ◽  
...  
Keyword(s):  
Monitoring System ◽  
Electric Energy ◽  
Communication Mode ◽  
Energy Monitoring ◽  
Electric Parameter ◽  
Centralized Management ◽  
Electric Parameters ◽  
Intelligent Management ◽  
Spi Bus ◽  
Upper Computer

To solve the problems of bulky, high cost and difficult maintenance existing in the electric parameters monitoring system currently, a new electric energy monitoring system is designed. In the system, MAXQ3180 chip can collect the voltage, current, power factor, harmonic and other parameters of the load. Then the relevant data can be collected and sent to AT89S52 microcontroller through SPI bus to saving and manipulating. Meanwhile, to achieve the goal of decentralized control and centralized management, the system can exchange the relevant data with upper computer by CAN bus communication mode. Then the accurate measurement and intelligent management of the electric parameter can be achieved.

Sign in / Sign up

Export Citation Format

Share Document