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

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

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v11.i2.pp637-644 ◽

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.

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Wireless Home Energy Meter Manager using Android: A Preliminary study

ELEKTRIKA- Journal of Electrical Engineering ◽

10.11113/elektrika.v18n3-2.197 ◽

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.

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

Journal of Global Information Management ◽

10.4018/jgim.2020100102 ◽

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.

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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 ◽

10.3390/pr9091539 ◽

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

Research Anthology on Clean Energy Management and Solutions ◽

10.4018/978-1-7998-9152-9.ch039 ◽

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

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

IoT ◽

10.3390/iot1010006 ◽

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.

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

Energies ◽

10.3390/en14030719 ◽

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.

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An optimisation-based energy disaggregation algorithm for low frequency smart meter data

Energy Informatics ◽

10.1186/s42162-019-0089-8 ◽

2019 ◽

Vol 2 (S1) ◽

Cited By ~ 2

Author(s):

Cristina Rottondi ◽

Marco Derboni ◽

Dario Piga ◽

Andrea Emilio Rizzoli

Keyword(s):

Energy Consumption ◽

State Of The Art ◽

Low Frequency ◽

Smart Meter ◽

Washing Machine ◽

Total Energy Consumption ◽

Energy Disaggregation ◽

Load Monitoring ◽

Individual Contributions ◽

The Individual

Abstract An algorithm for the non-intrusive disaggregation of energy consumption into its end-uses, also known as non-intrusive appliance load monitoring (NIALM), is presented. The algorithm solves an optimisation problem where the objective is to minimise the error between the total energy consumption and the sum of the individual contributions of each appliance. The algorithm assumes that a fraction of the loads present in the household is known (e.g. washing machine, dishwasher, etc.), but it also considers unknown loads, treating them as a single load. The performance of the algorithm is then compared to that obtained by two state of the art disaggregation approaches implemented in the publicly available NILMTK framework. The first one is based on Combinatorial Optimization, the second one on a Factorial Hidden Markov Model. The results show that the proposed algorithm performs satisfactorily and it even outperforms the other algorithms from some perspectives.

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Smart Meter Awareness in Italy, Ancona

Smart and Sustainable Planning for Cities and Regions - Green Energy and Technology ◽

10.1007/978-3-030-57764-3_4 ◽

2021 ◽

pp. 47-66

Author(s):

Vasileios Ntouros ◽

Nikolaos Kampelis ◽

Martina Senzacqua ◽

Theoni Karlessi ◽

Margarita-Niki Assimakopoulos ◽

...

Keyword(s):

Energy Consumption ◽

Smart Grid ◽

Energy Use ◽

Second Generation ◽

Energy Savings ◽

General View ◽

Smart Meter ◽

Accurate Data ◽

Energy Usage ◽

Smart Meters

AbstractSmart meters, one of the crucial enablers of the smart-grid concept and cornerstones in smart planning for cities, offer the opportunity for consumers to address their energy consumption effectively through timely and accurate data on their energy usage. However, previous studies have shown that smart meters may not lead to the desired energy savings unless actively used by households. To this end, the research presented in this paper investigates the penetration of smart meters at community level and explores how such a metering system can help people to understand and manage their energy use better. It examines the awareness about smart meters, looks into their presence in current accommodation and focuses on the views people have about smart meters. For this purpose, a questionnaire was prepared and distributed to a group of individuals residing in the wide area of Ancona province in Italy. Although the deployment of modern second-generation smart meters started in 2017 replacing the outdated smart meters massively installed in the 2000s, the results show low-to-moderate levels of awareness of modern smart meters among the respondents and a low presence of second-generation metering devices in their current accommodation. However, the general view expressed by the participants about smart meters is positive. The findings demonstrate that respondents are in need not only of a gauge that measures energy consumption but also of a tool that assists them to manage effectively their energy use.

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SMART METER CONFIGURATION FOR BETTER ENERGY CONSUMPTION WITH FAULT DETECTION

i-manager s Journal on Power Systems Engineering ◽

10.26634/jps.7.1.16502 ◽

2019 ◽

Vol 7 (1) ◽

pp. 12

Author(s):

PARAMANIK SAYAN ◽

KUSHARY INDRANIL ◽

SARKER KRISHNA ◽

◽

...

Keyword(s):

Energy Consumption ◽

Fault Detection ◽

Smart Meter

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