scholarly journals Mobile Application for Electric Power Monitoring on Energy Consumptions at a Campus University

2018 ◽  
Vol 11 (2) ◽  
pp. 637
Author(s):  
Murizah Kassim ◽  
Maisarah Abdul Rahman ◽  
Cik Ku Haroswati Che Ku Yahya ◽  
Azlina Idris
Keyword(s):  
Energy Consumption ◽  
Electric Power ◽  
Mobile Applications ◽  
Working Hours ◽  
Mobile App ◽  
Smart Meter ◽  
University Campus ◽  
Smart Meters ◽  
Power Monitoring ◽  
Energy Consumptions

This paper presents a research on electric power monitoring prototype mobile applications development on energy consumptions in a university campus. Electric power energy consumptions always are the issue of monitoring usage especially in a broad environment. University campus faces high used of electric power, thus crucial analysis on cause of the usage is needed. This research aims to analyses electric power usage in a university campus where implemented of few smart meters is installed to monitor five main buildings in a campus university. A Monitoring system is established in collecting electric power usage from the smart meters. Data from the smart meter then is analyzed based on energy consume on 5 buildings. Results presents graph on the power energy consume and presented on mobile applications using Live Code coding. The methodology involved the setup of the smart meters, monitoring and data collected from main smart meters, analyzed electrical consumptions for 5 buildings and mobile system development to monitor. A Live Code mobile app is designed then data collected from smart meter using ION software is published in graphs. Results presents the energy consumed for 5 building during day and night. Details on maximum and minimum energy consumption presented that show load of energy used in the campus. Result present Tower 1 saved most eenergy at night which is 65% compared to block 3 which is 8% saved energy although block 3 presents the lowest energy consumption in the working hours and non-working hours. This project is significant that can help campus facility to monitor electric power used thus able to control possible results in future implementations.

scholarly journals Wireless Home Energy Meter Manager using Android: A Preliminary study

2019 ◽  
Vol 18 (3-2) ◽  
pp. 32-36
Author(s):  
Sh. Nurul Hidayah Wan Julihi ◽  
Ili Najaa Aimi Mohd Nordin ◽  
Muhammad Rusydi Muhammad Razif ◽  
Amar Faiz Zainal Abidin
Keyword(s):  
Energy Consumption ◽  
Personal Information ◽  
Current Sensor ◽  
Smart Meter ◽  
Smart Meters ◽  
Long Distance ◽  
Energy Meter ◽  
Utility Companies ◽  
Preliminary Study ◽  
To Receive

Manual home energy meter reading and billing had caused inconvenience to the utility companies due to lack of manpower to read the energy meter at each household especially in the remote area, explains the increasing number of smart meter reader in the current market. Most of the smart meters in the market do not offer safety of privacy of consumers’ personal information since the data of electricity usage is being transferred digitally to the utility companies for more accurate bills calculation. Plus, the smart meter system is also a bit pricey to be installed in the rural area. Therefore, a private system that able to read energy consumption from a DC load and calculate its bill according to the tariff is proposed. Value of current is being obtained by using ACS712 current sensor. Hall circuit in the current sensor will converts magnetic field into a proportional voltage. The proposed system allows energy meter monitoring from an Android-based smartphone by displaying the real-time energy consumption and bill on Blynk application. An interface of Blynk is developed and connected to WiFi module, ESP8266 for visualizing the energy consumption of the DC load. In conclusion, the Energy Meter transmitter part able to read, calculate and transmit value of energy consumption and current bills to the Blynk application and Blynk application able to receive and show all the data transmitted at the present time. This system will be further improved for long-distance monitoring of electrical appliances used at home.

scholarly journals The Impact of Smart Meter Installation on Attitude Change Towards Energy Consumption Behavior Among Northern Ireland Households

2020 ◽  
Vol 28 (4) ◽  
pp. 21-37
Author(s):  
Roya Gholami ◽  
Ali Emrouznejad ◽  
Yazan Alnsour ◽  
Hasan B. Kartal ◽  
Julija Veselova
Keyword(s):  
Energy Consumption ◽  
Northern Ireland ◽  
Attitude Change ◽  
Deep Understanding ◽  
Smart Meter ◽  
Smart Meters ◽  
Consumption Behavior ◽  
Energy Management Systems ◽  
Energy Consumption Patterns ◽  
The Impact

The continuous development of energy management systems, coupled with a growing population, and increasing energy consumption, highlights the necessity to develop a deep understanding of household energy consumption behavior and interventions that facilitate behavioral change. Using a data mining segmentation technique, 2,505 Northern Ireland households were segmented into four distinctive profiles, based on their energy consumption patterns, socio-demographic, and dwelling characteristics. The change in attitude towards energy consumption behavior was analyzed to evaluate the impact of smart meter feedback as well. The key finding was 81% of trial participants perceived smart meters to be helpful in reducing their energy consumption. In addition, we found that the potential to reduce energy bills and environmental concerns were the strongest motivations for behavior change.

scholarly journals Mobile Applications at a Mega University: Anadolu University Campus App

2015 ◽  
Vol 10 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Ozgur Yilmazel ◽  
Erk Ekin
Keyword(s):  
Mobile Applications ◽  
Mobile App ◽  
Mobile Technologies ◽  
High Rate ◽  
Educational Institutions ◽  
University Campus ◽  
User Community ◽  
The University ◽  
Insight Into

This paper explores the use of mobile applications to aid on-campus and off-campus students at a mega university. Anadolu University — with over 1,900,000 students enrolled from over 30 countries — is the world's second largest university by enrolment(List of largest universities by enrolment, 2014). From its early days, the Universityhas used various means to access its students. During the last decade,with the introduction of mobile technologies and smartphones that are connected everywhere, the expectations of students have changed. Students now expect to be in contact with their educational institutions without any barriers. Anadolu University released its first mobile campus app onaniOS platform in May 2012. Students adopted the App quickly and the mobile app user community requested new functionalities. Since then,the University has released three major and over 25 minor releases of the app on both iOS and Android smartphones. This paper describes the lifecycle of Anadolu Campus App and its evolution over the years. It has been widely acceptedby our students both on campus and off campus, andthe increasing number of users gives an insight into the high rate of adoption of smartphones.

scholarly journals Two-level clustering methodology for smart metering data

2020 ◽  
Vol 33 ◽  
Author(s):  
Leticia Arco García ◽  
Gladys María Casas Cardoso ◽  
Ann Nowé
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Smart Cities ◽  
Smart Metering ◽  
Business Analytics ◽  
Smart Meters ◽  
Clustering Approach ◽  
Energy Consumptions ◽  
General Business ◽  
Global And Local

Energy efficiency and sustainability are important factors to address in the context of smart cities. In this sense, a necessary functionality is to reveal various preferences, behaviors, and characteristics of individual consumers, considering the energy consumption information from smart meters. In this paper, we introduce a general methodology and a specific two-level clustering approach that can be used to group, considering global and local features, energy consumptions and productions of households. Thus, characteristic load and production profiles can be determined for each consumer and prosumer, respectively. The obtained results will be generally applicable and will be useful in a general business analytics context.

scholarly journals Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1539
Author(s):  
Yu-Chen Hu ◽  
Yu-Hsiu Lin ◽  
Harinahalli Lokesh Gururaj
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Smart Meter ◽  
Energy Decomposition ◽  
Smart Meters ◽  
Electrical Grid ◽  
Electric Energy Consumption ◽  
Partitional Clustering ◽  
Neuro Fuzzy ◽  
Consumption Data

The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-computing-accelerated evolutionary computing, that performs energy decomposition on smart meter data in residential demand-side management, where a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset is used to experimentally test the presented model to classify the On/Off status of monitored electrical appliances. As shown in this research, the presented AI model is effective at providing energy decomposition for domestic consumers. Further, energy decomposition can be provided for industrial as well as commercial consumers.

The Impact of Smart Meter Installation on Attitude Change Towards Energy Consumption Behavior Among Northern Ireland Households

2021 ◽  
pp. 925-943
Author(s):  
Roya Gholami ◽  
Ali Emrouznejad ◽  
Yazan Alnsour ◽  
Hasan B. Kartal ◽  
Julija Veselova
Keyword(s):  
Energy Consumption ◽  
Northern Ireland ◽  
Attitude Change ◽  
Deep Understanding ◽  
Smart Meter ◽  
Smart Meters ◽  
Consumption Behavior ◽  
Energy Management Systems ◽  
Energy Consumption Patterns ◽  
The Impact

The continuous development of energy management systems, coupled with a growing population, and increasing energy consumption, highlights the necessity to develop a deep understanding of household energy consumption behavior and interventions that facilitate behavioral change. Using a data mining segmentation technique, 2,505 Northern Ireland households were segmented into four distinctive profiles, based on their energy consumption patterns, socio-demographic, and dwelling characteristics. The change in attitude towards energy consumption behavior was analyzed to evaluate the impact of smart meter feedback as well. The key finding was 81% of trial participants perceived smart meters to be helpful in reducing their energy consumption. In addition, we found that the potential to reduce energy bills and environmental concerns were the strongest motivations for behavior change.

scholarly journals Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption

IoT ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 92-108
Author(s):  
William Hurst ◽  
Casimiro A. Curbelo Montañez ◽  
Nathan Shone
Keyword(s):  
Energy Consumption ◽  
Spatial Clustering ◽  
Smart Meter ◽  
Energy Usage ◽  
Smart Meters ◽  
Time Pattern ◽  
Behavioural Patterns ◽  
Utility Companies ◽  
The Uk ◽  
The Internet Of Things

Smart meters have become a core part of the Internet of Things, and its sensory network is increasing globally. For example, in the UK there are over 15 million smart meters operating across homes and businesses. One of the main advantages of the smart meter installation is the link to a reduction in carbon emissions. Research shows that, when provided with accurate and real-time energy usage readings, consumers are more likely to turn off unneeded appliances and change other behavioural patterns around the home (e.g., lighting, thermostat adjustments). In addition, the smart meter rollout results in a lessening in the number of vehicle callouts for the collection of consumption readings from analogue meters and a general promotion of renewable sources of energy supply. Capturing and mining the data from this fully maintained (and highly accurate) sensing network, provides a wealth of information for utility companies and data scientists to promote applications that can further support a reduction in energy usage. This research focuses on modelling trends in domestic energy consumption using density-based classifiers. The technique estimates the volume of outliers (e.g., high periods of anomalous energy consumption) within a social class grouping. To achieve this, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and Local Outlier Factor (LOF) demonstrate the detection of unusual energy consumption within naturally occurring groups with similar characteristics. Using DBSCAN and OPTICS, 53 and 208 outliers were detected respectively; with 218 using LOF, on a dataset comprised of 1,058,534 readings from 1026 homes.

scholarly journals Nonintrusive Load Management Based on Distributed Edge and Secure Key Agreement

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jing Zhang ◽  
Qi Liu ◽  
Lu Chen ◽  
Ye Tian ◽  
Jun Wang
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Smart Home ◽  
Key Agreement ◽  
Load Management ◽  
Smart Meters ◽  
Energy Monitoring ◽  
Power Monitoring ◽  
Smart Home Appliances ◽  
The Relationship

With the advancement of national policies and the rise of Internet of things (IoT) technology, smart meters, smart home appliances, and other energy monitoring systems continue to appear, but due to the fixed application scenarios, it is difficult to apply to different equipment monitoring. At the same time, the limited computing resources of sensing devices make it difficult to guarantee the security in the transmission process. In order to help users better understand the energy consumption of different devices in different scenarios, we designed a nonintrusive load management based on distributed edge and secure key agreement, which uses narrowband Internet of things (NB-IoT) for transmission and uses edge devices to forward node data to provide real-time power monitoring for users. At the same time, we measured the changes of server power under different behaviors to prepare for further analysis of the relationship between server operating state and energy consumption.

scholarly journals Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2485
Author(s):  
Pascal A. Schirmer ◽  
Iosif Mporas ◽  
Akbar Sheikh-Akbari
Keyword(s):  
Energy Consumption ◽  
Identification Accuracy ◽  
Distance Measures ◽  
Smart Meter ◽  
Video Content ◽  
Smart Meters ◽  
Energy Disaggregation ◽  
Content Identification ◽  
Energy Signal ◽  
Monitor Device

Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task, a smart meter must be used for load forecasting, the reduction in consumer bills as well as the reduction in grid distortions. Smart meters can be used to disaggregate the energy consumption at the device level. In this paper, we investigated the potential of identifying the multimedia content played by a TV or monitor device using the central house’s smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed architecture was based on the elastic matching of aggregated energy signal frames with 20 reference TV channel signals. Different elastic matching algorithms, which use symmetric distance measures, were used with the best achieved video content identification accuracy of 93.6% using the MVM algorithm.

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