FIXED MONITORING SYSTEM FOR MONITORING GAS GENERATED IN WASTE LANDFILL

2018 ◽  
Vol 7 (3) ◽  
Author(s):  
Heru Dwi Wahjono
Keyword(s):  
Monitoring System ◽  
Landfill Gas ◽  
Electrical Energy ◽  
Gas Production ◽  
High Concentration ◽  
Energy Monitoring ◽  
Waste Landfill ◽  
Methane Gas ◽  
Energy Conversion Process ◽  
Alternative Sources

Gas produced by landfill is bio-gas that has a high concentration of methane that could cause global warming if not controlled. Good management of a landfill in controling the production of methane gas, can be used as an alternative sources of energy to produce electrical energy. Monitoring of methane gas production needs to be done continuously for the energy conversion process can take place perfectly without polluting the environment. Fixed monitoring system described in this paper is the result of design and development including hardware and software of monitoring technology for methane gas to facilitate research in doing observations of a landfill performance.keywords : final disposal, landfill gas, methane, landfill performance, fixed landfill gas monitoring system

scholarly journals Regularly calibrating an energy monitoring system ensures accuracy

2019 ◽  
pp. 37-45
Author(s):  
Arthur J Swart ◽  
Pierre E Hertzog
Keyword(s):  
Monitoring System ◽  
Electrical Energy ◽  
Monitoring Systems ◽  
Data Logging ◽  
Energy Monitoring ◽  
Pv Systems ◽  
Pv Module ◽  
Limited Life ◽  
Pv Modules ◽  
Lab View

Energy monitoring systems are being reported on more and more as consumers wish to determine the amount of energy produced and used by various renewable energy systems. Added to this is improving the overall systems’ efficiency and identifying any potential concerns. The purpose of the paper is to show the importance of correctly calibrating such energy monitoring systems on a regular basis, in order to validate any future measurements as being reliable. In this study, three 10 W PV modules are used with their own respective LED loads to extract the maximum possible amount of electrical energy during the day. No storage systems are used due to their limited life-cycle and variability. An Arduino microcontroller is used as the data logging interface between the PV systems and a PC running Lab VIEW software, which acts as the visual interface and recording system. Calibration is done in Lab VIEW to account for system losses. Results indicate that three identical PV systems can be calibrated to produce the same results, with variability of less than 1%. Higher variabilities point to inconsistencies in the PV modules, even if they originate from the same manufacturer. A key recommendation is to perform an annual calibration of the monitoring system, which primarily accounts for PV module degradation.

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.

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