Analytics framework for optimal smart meters data processing

Alquthami, Thamer; Alamoudi, Ahmed; Alsubaie, Abdullah; Jaber, Abdulrahman Bin; Alshlwan, Nassir; Anwar, Murad; Husaien, Shafi Al

doi:10.1007/s00202-020-00949-0

Cited by 10 publications

(9 citation statements)

References 21 publications

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“…Due to additional information, end users are able to devise optimal decisions regarding load shifting for economic and other benefits (Albu et al, 2016;Bahmanyar et al, 2016;Sama et al, 2019a). A smart meter is the blended version of the latest techniques of computer science and instrumental and measurement units (Borges et al, 2015;Shateri et al, 2020;Alquthami et al, 2020;Alkawsi et al, 2021). Hence, smart meters are the foundation of EMSs.…”

Section: Smart Metersmentioning

confidence: 99%

Utilization of ICT and AI techniques in harnessing residential energy consumption for an energy-aware smart city: A review

al.¹

2021

Int. j. adv. appl. sci.

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Fusion of Information and Communication Technologies (ICT) in traditional grid infrastructure makes it possible to share certain messages and information within the system that leads to optimized use of energy. Furthermore, using Computational Intelligence (CI) in the said domain opens new horizons to preserve electricity as well as the price of consumed electricity effectively. Hence, Energy Management Systems (EMSs) play a vital role in energy economics, consumption efficiency, resourcefulness, grid stability, reliability, and scalability of power systems. The residential sector has its high impact on global energy consumption. Curtailing and shifting load of the residential sector can result in solving major global problems and challenges. Moreover, the residential sector is more flexible in reshaping power consumption patterns. Using Demand Side Management (DSM), end users can manipulate their power consumption patterns such that electricity bills, as well as Peak to Average Ratio (PAR), are reduced. Therefore, it can be stated that Home Energy Management Systems (HEMSs) is an important part of ground-breaking smart grid technology. This article gives an extensive review of DSM, HEMS methodologies, techniques, and formulation of optimization problems. Concluding the existing work in energy management solutions, challenges and issues, and future research directions are also presented.

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Section: Smart Metersmentioning

confidence: 99%

Utilization of ICT and AI techniques in harnessing residential energy consumption for an energy-aware smart city: A review

al.¹

2021

Int. j. adv. appl. sci.

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“…Smart meters are a cornerstone of these smart grids and are thus installed in an increasing number worldwide. They record and transmit a variety of data such as voltage, reactive power, or the electricity consumption of consumers [2]. The collected data is an essential input to various applications supporting and enabling the transition to energy systems from renewable energies.…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

“…It is reduced by problems causing, for example, noise and outliers in the data [7], [8]. For further processing, outliers in particular are often detected and labeled as missing values as a first step [2], [9]. The reliability of data transmission and data storage, however, mainly relates to the completeness of the recorded data.…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

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Data-Driven Copy-Paste Imputation for Energy Time Series

Weber

Turowski

Çakmak

et al. 2021

IEEE Trans. Smart Grid

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A cornerstone of the worldwide transition to smart grids are smart meters. Smart meters typically collect and provide energy time series that are vital for various applications, such as grid simulations, fault-detection, load forecasting, load analysis, and load management. Unfortunately, these time series are often characterized by missing values that must be handled before the data can be used. A common approach to handle missing values in time series is imputation. However, existing imputation methods are designed for power time series and do not take into account the total energy of gaps, resulting in jumps or constant shifts when imputing energy time series. In order to overcome these issues, the present paper introduces the new Copy-Paste Imputation (CPI) method for energy time series. The CPI method copies data blocks with similar characteristics and pastes them into gaps of the time series while preserving the total energy of each gap. The new method is evaluated on a real-world dataset that contains six shares of artificially inserted missing values between 1 and 30%. It outperforms the three benchmark imputation methods selected for comparison. The comparison furthermore shows that the CPI method uses matching patterns and preserves the total energy of each gap while requiring only a moderate run-time.

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“…Por outro lado, a grande popularidade dos medidores inteligentes faz com que uma grande quantidade de dados de consumo de eletricidade sejam coletados. Isso significa que as concessionárias de distribuição de energia elétrica precisam lidar com uma quantidade considerável de dados [2]. É fornecida a coleta de dados com maior eficácia do serviço, integralidade e acesso universal a partir de medidores inteligentes, produzindo informações valiosas sobre o consumo de eletricidade, comportamentos e estilos de vida do consumidor [24].…”

Section: Introductionunclassified

Greedy Genetic Algorithm for the Data Aggregator Positioning Problem in Smart Grids

Lauar

Mestria

2021

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In this work, we present a metaheuristic based on the genetic and greedy algorithms to solve an application of the set covering problem (SCP), the data aggregator positioning in smart grids. The GGH (Greedy Genetic Hybrid) is structured as a genetic algorithm, but it has many modifications compared to the classic version. At the mutation step, only columns included in the solution can suffer mutation and be removed. At the recombination step, only columns from the parent’s solutions are available to generate the offspring. Moreover, the greedy algorithm generates the initial population, reconstructs solutions after mutation, and generates new solutions from the recombination step. Computational results using OR-Library problems showed that the GGH reached optimal solutions for 40 instances in a total of 75 and, in the other instances, obtained good and promising values, presenting a medium gap of 1,761%.

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Analytics framework for optimal smart meters data processing

Cited by 10 publications

References 21 publications

Utilization of ICT and AI techniques in harnessing residential energy consumption for an energy-aware smart city: A review

Utilization of ICT and AI techniques in harnessing residential energy consumption for an energy-aware smart city: A review

Data-Driven Copy-Paste Imputation for Energy Time Series

Greedy Genetic Algorithm for the Data Aggregator Positioning Problem in Smart Grids

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