Appling the WSN Concept in Implementing an Uninterrupted Solar Energy Monitoring System

2012 ◽  
Vol 5 (3) ◽  
pp. 238-243
Author(s):  
Joy I. -Z. Chen
Author(s):  
Aoife Hegarty ◽  
Guy Westbrook ◽  
Damien Glynn ◽  
Declan Murray ◽  
Edin Omerdic ◽  
...  

Author(s):  
Gaikar Vilas Bhau ◽  
Radhika Gautamkumar Deshmukh ◽  
T. Rajasanthosh kumar ◽  
Subhadip Chowdhury ◽  
Y. Sesharao ◽  
...  

2021 ◽  
Author(s):  
Zheng Wang ◽  
Xuezeng Jia

Aiming at the problems of low utilization rate of solar energy and poor anti-interference ability of tracking structure solar energy control system in fixed structure solar energy device, this paper designs a dual axis high-precision solar tracking system based on four quadrant rule. The system adopts two ways: automatic tracking and manual correction. The system uses four photoresistors as detection elements, uses the four quadrant principle to judge the tracking offset angle, and drives two-dimensional two axis stepper motor through STC89C52 processor to achieve the purpose of vertical angle, so as to ensure that the solar panel is always in the state of maximum light receiving surface; When the system is disturbed, it can be judged according to the change of the photosensitive resistance in the energy monitoring system, and the artificial correction can be realized by modulating the size of the divider resistance, which can basically achieve 360° Automatic rotation tracking. In addition, the energy monitoring system based on LabView is designed. Through the real system analysis, it can be concluded that the photoelectric energy conversion rate of the fixed solar device is increased by 32.4%.


Author(s):  
Mopuri Deepika ◽  
Merugu Kavitha ◽  
N. S. Kalyan Chakravarthy ◽  
J. Srinivas Rao ◽  
D. Mohan Reddy ◽  
...  

2021 ◽  
Vol 17 (3) ◽  
pp. 1-20
Author(s):  
Vanh Khuyen Nguyen ◽  
Wei Emma Zhang ◽  
Adnan Mahmood

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.


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