scholarly journals Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 719
Author(s):  
Benjamin Völker ◽  
Andreas Reinhardt ◽  
Anthony Faustine ◽  
Lucas Pereira
Keyword(s):  
Energy Consumption ◽  
Data Processing ◽  
Electricity Consumption ◽  
Data Communication ◽  
Electrical Energy ◽  
Use Cases ◽  
Smart Meter ◽  
Smart Meters ◽  
Electricity Theft ◽  
Generation Scheduling

The key advantage of smart meters over traditional metering devices is their ability to transfer consumption information to remote data processing systems. Besides enabling the automated collection of a customer’s electricity consumption for billing purposes, the data collected by these devices makes the realization of many novel use cases possible. However, the large majority of such services are tailored to improve the power grid’s operation as a whole. For example, forecasts of household energy consumption or photovoltaic production allow for improved power plant generation scheduling. Similarly, the detection of anomalous consumption patterns can indicate electricity theft and serve as a trigger for corresponding investigations. Even though customers can directly influence their electrical energy consumption, the range of use cases to the users’ benefit remains much smaller than those that benefit the grid in general. In this work, we thus review the range of services tailored to the needs of end-customers. By briefly discussing their technological foundations and their potential impact on future developments, we highlight the great potentials of utilizing smart meter data from a user-centric perspective. Several open research challenges in this domain, arising from the shortcomings of state-of-the-art data communication and processing methods, are furthermore given. We expect their investigation to lead to significant advancements in data processing services and ultimately raise the customer experience of operating smart meters.

scholarly journals Smart metering system data analytics platform using multicore edge computing

2021 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Juan C. Olivares-Rojas ◽  
Enrique Reyes-Archundia ◽  
José A. Gutiérrez-Gnecchi ◽  
Ismael Molina-Moreno ◽  
Adriana C. Téllez-Anguiano ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Smart Grid ◽  
Data Processing ◽  
Data Analytics ◽  
Electricity Consumption ◽  
Edge Computing ◽  
Smart Meter ◽  
Smart Metering ◽  
Smart Meters ◽  
System Data

The smart grid revolution has only been possible, thanks to the development and proliferation of smart meters. The increasingly growing computing capabilities for Internet of Things devices have made it possible for data to be processed directly from the devices where it is produced; this has been called edge computing. Edge computing is allowing the smart grid to become increasingly intelligent to solve problems that make electricity consumption more efficient and environmentally friendly. This work presents the implementation of a smart metering system that allows data analytics using a multiprocessing architecture directly on the smart meter. The results show that the development of smart meters with data analytics capabilities at the edge is a reality today, and the use of multiprocessing permits the improvement of data processing.

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.

Smart Metering for Applications

2014 ◽  
Vol 960-961 ◽  
pp. 823-827
Author(s):  
Ying Pan ◽  
Bo Jiang
Keyword(s):  
Smart Grid ◽  
Electrical Energy ◽  
Smart Meter ◽  
Smart Metering ◽  
Power Network ◽  
Energy Usage ◽  
Smart Meters ◽  
Additional Information ◽  
Energy Efficiency Improvement ◽  
Energy Meter

As an important part of Smart Grid, smart metering attracts more and more attention all over the world. It is the way for energy consumer to sense the benefit of smart grid directly. Smart meter is an advanced energy meter that measures consumption of electrical energy providing additional information compared to a conventional energy meter. This paper discusses various applications and technologies that can be integrated with a smart meter. Smart meters can be used not only from the supply side monitoring but also for the demand side management as well. It plays an important role to monitor the performance and the energy usage of the grid loadings and power quality. In addition, This paper gives a comprehensive view on the benefit of smart metering in power network such as energy efficiency improvement.

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 Comparative Analysis of Two Urban Microclimates: Energy Consumption and Greenhouse Gas Emissions

2019 ◽  
Vol 11 (7) ◽  
pp. 2045 ◽  
Author(s):  
Néstor Santillán-Soto ◽  
O. García-Cueto ◽  
Alejandro Lambert-Arista ◽  
Sara Ojeda-Benítez ◽  
Samantha Cruz-Sotelo
Keyword(s):  
Energy Consumption ◽  
Air Conditioning ◽  
Scientific Evidence ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Air Conditioner ◽  
Comparative Performance ◽  
Urban Complex ◽  
Arid Desert ◽  
Urban Microclimates

This paper presents a hypothetical and comparative performance of a 5 ton air conditioner (AC) operating in two zones in different urban microclimates for 25 days. One site represents a type of homogeneous planned urbanism and the other is a traditional heterogeneous zone. Air temperature data was collected and then processed using a linear regression model included in the operating manual of the AC in order to obtain their energy consumption. Results indicate that for an area with 500 homes, a traditional urban complex requires 12,350 kWh of electrical energy more than a planned zone (1.89%). This extra energy amounts up to $1180 and adds 9191 kg of CO2 to the atmosphere. The increased energy consumption has implications that increase the cost and environmental aspects of two urban microclimates, so that urbanization without planning is less friendly to the environment. In this sense, this study highlights the effects of urban microclimates on domestic electricity consumption from air conditioning. In addition, for a city with an arid desert climate, the variation in electricity consumption is associated with changes in the urban mosaic. The results found represent scientific evidence that can be used as a reference to establish public policies that could be incorporated into the local construction regulations, oriented to reduce the energy consumption associated with the use of air conditioning equipment.

scholarly journals MACHINE LEARNING AND IOT FOR SMART GRID

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 233-240
Author(s):  
M. Fouad ◽  
R. Mali ◽  
A. Lmouatassime ◽  
M. Bousmah
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Smart Grid ◽  
Power Distribution ◽  
Short Term Memory ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Smart Meters ◽  
Power Distribution Networks ◽  
Electricity Grid

Abstract. The current electricity grid is no longer an efficient solution due to increasing user demand for electricity, old infrastructure and reliability issues requires a transformation to a better grid which is called Smart Grid (SG). Also, sensor networks and Internet of Things (IoT) have facilitated the evolution of traditional electric power distribution networks to new SG, these networks are a modern electricity grid infrastructure with increased efficiency and reliability with automated control, high power converters, modern communication infrastructure, sensing and measurement technologies and modern energy management techniques based on optimization of demand, energy and availability network. With all these elements, harnessing the science of Artificial Intelligence (AI) and Machine Learning (ML) methods become better used than before for prediction of energy consumption. In this work we present the SG with their architecture, the IoT with the component architecture and the Smart Meters (SM) which play a relevant role for the collection of information of electrical energy in real time, then we treat the most widely used ML methods for predicting electrical energy in buildings. Then we clarify the relationship and interaction between the different SG, IoT and ML elements through the design of a simple to understand model, composed of layers that are grouped into entities interacting with links. In this article we calculate a case of prediction of the electrical energy consumption of a real Dataset with the two methods Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), given their precision performances.

scholarly journals Pengembangan Sistem Pemantauan Konsumsi Energi Rumah Tangga Berbasis Internet of Things (IoT)

2018 ◽  
Vol 6 (3) ◽  
pp. 357
Author(s):  
HARTONO BUDI SANTOSO ◽  
SAPTO PRAJOGO ◽  
SRI PARYANTO MURSID
Keyword(s):  
Energy Consumption ◽  
Measurement Accuracy ◽  
Measurement Data ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Average Error ◽  
Android Application ◽  
Wireless Technology ◽  
Power Meter ◽  
Communication Device

ABSTRAKPenghematan pada konsumsi listrik rumah tangga akan memberikan dampak pada konsumsi listrik nasional. Penelitian menunjukkan pemantauan terhadap konsumsi listrik rumah tangga akan memberikan dampak pada penghematan konsumsi listrik hingga 30%. Beberapa penelitian terkait pengembangan pemantauan terhadap konsumsi listrik rumah tangga masih menunjukkan hasil yang kurang memuaskan. Pada penelitian ini akan dikembangkan sistem pemantauan energi khususnya untuk beban rumah tangga berbasis teknologi IoT, sehingga dapat dilakukan pemantauan menggunaan energi listrik rumah tangga menggunakan aplikasi android di perangkat komunikasi telepon seluler (ponsel). Hasil pengujian akurasi pengukuran, dilakukan dengan membandingkan data pengukuran dengan alat ukur lain, menunjukkan pembacaan arus memiliki rata-rata error sebesar 0% sementara pembacaan tegangan memiliki rata-rata error sebesar 0,06%.Kata kunci: IoT, power meter, power monitor, konsumsi energi ABSTRACTThe savings on household electricity consumption will have an impact on national electricity consumption. Research shows that monitoring of household electricity consumption will have an impact on saving electricity consumption up to 30%. Direct monitoring starts from using cable to wireless technology. Some studies realated to developments of energy consumption monitoring still show unsatisfactory results.In this research will be developed energy monitoring system especially for household load based on IoT technology, so that can be monitored the use of household electrical energy using android application in communication device, handphone. The result of measurement measurement accuracy is done by comparing measurement data with other measuring instrument, indicating current reading has an average error of 0% while the voltage reading has an average error of 0.06%.Keywords: IoT, power meter, power Monitor, energy consumption

scholarly journals Prospects for the use of solar energy in the Irkutsk Region and the Republic of Buryatia

2021 ◽  
Vol 289 ◽  
pp. 05002
Author(s):  
О.S. Kuznetsova ◽  
V.V. Khanaev
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Power Plants ◽  
Renewable Energy Sources ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Energy Sources ◽  
Irkutsk Region ◽  
The Republic ◽  
Distribution Generation

Due to the ever-increasing volume of energy consumption, the number of power plants capable of generating the necessary amount of electrical energy inevitably increases. The development and construction of new renewable energy sources and distribution generation facilities, the increase in electricity consumption and the growth of the tariff stimulates the search for effective technological solutions. Also in connection with the increasing popularity and improvement of technologies, there is a natural need to assess the prospects and potential opportunities of SES in the region as a whole, and for the Irkutsk region and the Republic of Buryatia, in particular.

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.

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