scholarly journals Sustainable Development with Smart Meter Data Analytics Using NoSQL and Self-Organizing Maps

2020 ◽  
Vol 12 (8) ◽  
pp. 3442
Author(s):  
Simona-Vasilica Oprea ◽  
Adela Bâra ◽  
Bogdan George Tudorică ◽  
Gabriela Dobrița (Ene)
Keyword(s):  
Data Analytics ◽  
Query Language ◽  
Clustering Algorithms ◽  
Electricity Consumption ◽  
Network Clustering ◽  
Smart Meter ◽  
Smart Meters ◽  
Self Organizing Maps ◽  
Consumption Data ◽  
Self Organizing

The smart metered electricity consumption data and high dimensional questionnaires provide useful information for designing the tariffs aimed at reducing electricity consumption and peak. The volume of data generated by smart meters for a sample of around four thousand residential consumers requires Not only Structured Query Language (NoSQL) solutions, data management and artificial neural network clustering algorithms, such as Self-Organizing Maps. In this paper, we propose a novel methodology that handles a large volume of data and extracts information from electricity consumption measured at 30 min and from complex questionnaires. Five three-level Time-of-Use tariffs are altered and investigated to minimize the consumers’ payment. Then, input data analysis revealed that the peak consumption is influenced by a segment of consumers that can be targeted to flatten the peak. Based on simulations, more than 23% of the peak consumption can be reduced by shifting it from peak to off-peak hours.

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 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.

Forecasting electricity consumption using nonlinear projection and self-organizing maps

2002 ◽  
Vol 48 (1-4) ◽  
pp. 299-311 ◽  
Author(s):  
A. Lendasse ◽  
J. Lee ◽  
V. Wertz ◽  
M. Verleysen
Keyword(s):  
Electricity Consumption ◽  
Self Organizing Maps ◽  
Nonlinear Projection ◽  
Self Organizing

scholarly journals ELECTROFACIES CLASSIFICATION OF PONTA GROSSA FORMATION BY MULTI-RESOLUTION GRAPH-BASED CLUSTERING (MRGC) AND SELF-ORGANIZING MAPS (SOM) METHODS

2020 ◽  
Vol 38 (1) ◽  
pp. 52
Author(s):  
Felipe Vasconcelos dos Passos ◽  
Marco Antonio Braga ◽  
Thiago Gonçalves Carelli ◽  
Josiane Branco Plantz
Keyword(s):  
Gamma Ray ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Resolution Graph ◽  
Lithology Prediction ◽  
Paraná Basin ◽  
Parana Basin ◽  
Graph Based Clustering ◽  
Self Organizing

ABSTRACT. In Ponta Grossa Formation, devonian interval of Paraná Basin, Brazil, sampling restrictions are frequent, and lithological interpretations from gamma ray logs are common. However, no single log can discriminate lithology unambiguously. An alternative to reduce the uncertainty of these assessments is to perform multivariate analysis of well logs using data clustering methods. In this sense, this study aims to apply two different clustering algorithms, trained with gamma ray, sonic and resistivity logs. Five electrofacies were differentiated and validated by core data. It was found that one of the electrofacies identified by the model was not distinguished by macroscopic descriptions. However, the model developed is sufficiently accurate for lithological predictions.Keywords: geophysical well logging, lithology prediction, Paraná Basin. CLASSIFICAÇÃO DE ELETROFÁCIES DA FORMAÇÃO PONTA GROSSA UTILIZANDO OS MÉTODOS MULTI-RESOLUTION GRAPH-BASED CLUSTERING (MRGC) E SELF-ORGANIZING MAPS (SOM)RESUMO. Na Formação Ponta Grossa, intervalo devoniano da Bacia do Paraná, Brasil, restrições de amostragem são frequentes e interpretações litológicas dos registros de raios gama são comuns. No entanto, nenhum perfil geofísico único pode discriminar litologias sem ambiguidade. Uma alternativa para reduzir a incerteza dessas avaliações é executar uma análise multivariada combinando vários perfis geofísicos de poços por meio de métodos de agrupamento de dados. Nesse sentido, este estudo tem como objetivo aplicar dois algoritmos de agrupamento aos registros de raios gama, sônico e resistividade para fins de predição litológica. Cinco eletrofácies foram diferenciadas e validadas por dados de testemunhos. Verificou-se que uma classe identificada pelo modelo não foi identificada por descrições macroscópicas. Porém, o modelo é suficientemente preciso para predições litológicas.Palavras-chave: geofísica de poços, predição litológica, correlação rocha-perfil, Bacia do Paraná.

Lifestyle Analysis from Household Electricity Consumption Data

2020 ◽  
Vol 24 (2) ◽  
pp. 214-220
Author(s):  
Yu Shirai ◽  
Shunichi Hattori ◽  
Yasufumi Takama ◽  
◽  
Keyword(s):  
Quality Of Life ◽  
Electricity Consumption ◽  
Ground Truth ◽  
Smart Meters ◽  
Positive Correlation ◽  
Aging Society ◽  
Consumption Data ◽  
Target Data

This paper aims to analyze the lifestyle of residents from household electricity consumption data. Improving QOL (Quality of Life) of elderlies has attracted attention in a super-aging society. It is known that the lifestyle of a person directly affects his / her health and QOL. Therefore, understanding a lifestyle is expected to be useful for providing various support for improving QOL, such as recommending adequate actions and daily habit. As a means for understanding residents’ lifestyle, this paper focuses on household electricity consumption data, which gets to be available with the spread of smart meters. The analysis is conducted by estimating the time of taking essential actions such as wake up and eating. As the target data has no ground truth, this paper also shows the result of an experiment on the detection of the essential actions. The analysis results reveal several findings which could be useful for improving QOL, such as positive correlation between regularity of dinner time and bedtime.

scholarly journals Mobile Anomaly Detection Based on Improved Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Chunyong Yin ◽  
Sun Zhang ◽  
Kwang-jun Kim
Keyword(s):  
Anomaly Detection ◽  
Mobile Devices ◽  
Clustering Algorithms ◽  
Recall Rate ◽  
Accuracy Rate ◽  
Optimal Method ◽  
Self Organizing Maps ◽  
Different Characteristics ◽  
Precision Rate ◽  
Self Organizing

Anomaly detection has always been the focus of researchers and especially, the developments of mobile devices raise new challenges of anomaly detection. For example, mobile devices can keep connection with Internet and they are rarely turned off even at night. This means mobile devices can attack nodes or be attacked at night without being perceived by users and they have different characteristics from Internet behaviors. The introduction of data mining has made leaps forward in this field. Self-organizing maps, one of famous clustering algorithms, are affected by initial weight vectors and the clustering result is unstable. The optimal method of selecting initial clustering centers is transplanted from K-means to SOM. To evaluate the performance of improved SOM, we utilize diverse datasets and KDD Cup99 dataset to compare it with traditional one. The experimental results show that improved SOM can get higher accuracy rate for universal datasets. As for KDD Cup99 dataset, it achieves higher recall rate and precision rate.

scholarly journals A Privacy-Preserving Noise Addition Data Aggregation Scheme for Smart Grid

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2972 ◽  
Author(s):  
Yuwen Chen ◽  
José-Fernán Martínez ◽  
Pedro Castillejo ◽  
Lourdes López
Keyword(s):  
Smart Grid ◽  
Personal Information ◽  
Homomorphic Encryption ◽  
Security Analysis ◽  
Electricity Consumption ◽  
Smart Meter ◽  
Smart Meters ◽  
Fine Grained ◽  
Aggregation Scheme ◽  
Reported Data

Smart meters are applied to the smart grid to report instant electricity consumption to servers periodically; these data enable a fine-grained energy supply. However, these regularly reported data may cause some privacy problems. For example, they can reveal whether the house owner is at home, if the television is working, etc. As privacy is becoming a big issue, people are reluctant to disclose this kind of personal information. In this study, we analyzed past studies and found that the traditional method suffers from a meter failure problem and a meter replacement problem, thus we propose a smart meter aggregation scheme based on a noise addition method and the homomorphic encryption algorithm, which can avoid the aforementioned problems. After simulation, the experimental results show that the computation cost on both the aggregator and smart meter side is reduced. A formal security analysis shows that the proposed scheme has semantic security.

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 Energy Analytics for Smart Meter Data using Consumer Centric Approach

2018 ◽  
Vol 7 (3.12) ◽  
pp. 656
Author(s):  
Nimala K ◽  
Thamizh Arasan. R
Keyword(s):  
Demand Forecasting ◽  
Electricity Consumption ◽  
Third Party ◽  
Smart Meter ◽  
Seasonal Factors ◽  
Consumption Data ◽  
Utility Companies ◽  
Energy Analytics ◽  
Interactive Application ◽  
And Control

A short-range residential consumer’s demand forecasting at the distinct and cumulative level, by an analysis of data using consumer based centric approach. Energy intake behavior might fluctuate among various seasonal factors; the consumed current will change from one season to other. So hereby we are building a model which helps to calculate future electricity consumption data from the obtain ability of past smart meter data. Currently utility companies accumulate the data, use it, share for further practice, and abandon usage data at their discretion, with no input from customers. In many cases, consumers do not even have entree to their own data. But in this project Consumer can have fast admittance and control over their individual data, and also helps to choose the familiar algorithms for the data analyze rather than including third party applications. By end of analyze technique, the analyzed output will be driven to some user interactive application by creating a Graphical User Interface.  

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