scholarly journals Energy Monitoring System Berbasis Web

2013 ◽  
Vol 4 (2) ◽  
pp. 867
Author(s):  
Novan Zulkarnain
Keyword(s):  
Monitoring System ◽  
Humidity Sensor ◽  
Energy Savings ◽  
Mineral Resources ◽  
Energy Monitoring ◽  
Web Based ◽  
Computer Data ◽  
Modbus Protocol ◽  
Design And Testing ◽  
Prototype Design

Government through the Ministry of Energy and Mineral Resources (ESDM) encourages the energy savings at whole buildings in Indonesia. Energy Monitoring System (EMS) is a web-based solution to monitor energy usage in a building. The research methods used are the analysis, prototype design and testing. EMSconsists of hardware which consists of electrical sensors, temperature-humidity sensor, and a computer. Data on EMS are designed using Modbus protocol, stored in MySQL database application, and displayed on charts through Dashboard on LED TV using PHP programming.

scholarly journals Energy Monitoring System for Existing Buildings in Indonesia

2018 ◽  
Vol 42 ◽  
pp. 01003
Author(s):  
Sentagi Sesotya Utami ◽  
Faridah ◽  
Na’im A. Azizi ◽  
Erlin Kencanawati ◽  
M. Akbar Tanjung ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Monitoring System ◽  
Local Governments ◽  
Energy Savings ◽  
Performance Testing ◽  
Cost Effective ◽  
Existing Buildings ◽  
Energy Monitoring ◽  
Building Permit ◽  
Significant Difference

Current studies conducted by JICA, AMPRI and IFC-World Bank, reported that large commercial buildings in Indonesia are not energy and water efficient. One of the cause is the lack of regulation. Meanwhile, effective regulations to reduce energy and water consumption are the concern mostly in a new building to obtain a building permit. This strategy is understandable as retrofitting existing buildings are often more difficult to be implemented, and enforcement is still a major issue in Indonesia. Local governments are currently working on streamlining building permit process as well as developing an online monitoring system for existing buildings. By applying a Building Energy Management System (BEMS) enables to reduce energy consumption up to 15%. An energy monitoring system was designed and installed through this research for Department of Nuclear Engineering and Engineering Physics (DNEEP) building, Faculty of Engineering, Universitas Gadjah Mada. It is a 20 years old two-story building used for educational activities, which consist of classrooms, laboratories, offices and storage spaces. An audit energy was done recently in 2015 where an energy consumption of 261.299,636 kWh/year.m2 was reported. In the existing condition, a power meter is inaccessible and therefore, the only feedback of occupancy behavior in the energy consumption is through the electricity bill. The previous study has shown that building occupants would behave more efficiently if the amount of energy used is notified, and the amount of energy savings are recorded. However, these energy monitoring systems are considered expensive and uniquely tailored for every building. This research aims to design and install a cost effective BEMS based on occupant’s satisfactory assessment of the lighting, acoustics, and air conditioning quality. The data will be used as a decision supporting system (DSS) by building management through the use of a GUI. The design of the interface was based on a survey result from the prospective users. Installed energy monitoring system uses a current sensor with an accuracy of 98% and a precision of 0.04 A while the voltage sensor with an accuracy of 98% and a precision of 0.58 V. The performance testing shows that the number of web clients influences delay of data transmission. The result of the survey shows that GUI is categorized as fair in design without a significant difference between the perceptions of users with and without survey supervision.

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

Web-based Riverbank Overflow Forecasting and Monitoring System

2020 ◽  
Author(s):  
Ma. Vienna R. Lozanes ◽  
Carlo C. Nunez ◽  
Ricardo O. Zapanta ◽  
Aldrin J. Soriano ◽  
Mary Grace P. Beano ◽  
...  
Keyword(s):  
Monitoring System ◽  
Web Based

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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