ecoMOD 1 Data Monitoring System : the Design and Installation of a Residential Energy Monitoring System

2007 ◽  
Author(s):  
Benjamin Wilbur Kidd
Keyword(s):  
Monitoring System ◽  
Data Monitoring ◽  
Residential Energy ◽  
Energy Monitoring

Low-cost non-intrusive residential energy monitoring system

2014 ◽  
Author(s):  
Akshaya Ravishankar ◽  
Anand Vignesh ◽  
Vikram Vel ◽  
Dhiwaakar Purusothaman S R R ◽  
Vineeth Vijayaraghavan
Keyword(s):  
Monitoring System ◽  
Low Cost ◽  
Residential Energy ◽  
Energy Monitoring

iTrack: A residential energy monitoring system tailored to meet local needs

2015 ◽  
Author(s):  
Khadijah Alsafwan ◽  
Fatimah Alshaer ◽  
Lolah Hakami ◽  
Khawlah Aseeri ◽  
Masoumah Aljishi ◽  
...  
Keyword(s):  
Monitoring System ◽  
Residential Energy ◽  
Energy Monitoring

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
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