Use of data from smart meters in optimal operation of distribution systems

Grigoraș, Gheorghe; Scarlatache, Florina

doi:10.1109/optim.2014.6850913

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…An important research topic used in optimal operation and planning of distribution networks by electric companies refers at peak load estimation [6]- [8]. This is corresponding particularly to household consumers, whose energy consumption is not usually measured with smart meters.…”

Section: Description Of Proposed Methodologymentioning

confidence: 99%

“…The advantage of the unsupervised learning methods is that interesting structures can be found directly from the data without any background knowledge. From this category, the best known methods are the clustering techniques [8], [15], [19], [20].…”

Section: Data Mining For Larmentioning

confidence: 99%

“…From these, the test based on the Silhouette Global Index calculation is one of the most used [8], [15], [19], [20]. This calculates the silhouette width for each sample, average silhouette width for each cluster and overall average silhouette width for the entire data set.…”

Section: Data Mining For Larmentioning

confidence: 99%

“…Italy o waited changes to the legislative and regu and in the past years a large number of [2]. Implementa might support the electricity c following problems: minimizin pilferages, clamping peak p generation efficiency, economic real time state estimation [8]-[10 Smart metering infrastructu produce enormous volumes challenges for utility providers: and how to extract information metering infrastructures prime of Mining Data techniques. E these techniques can lead to re aggregation and disaggregation peak load forecasting (PLF), support.…”

Section: Introductionmentioning

confidence: 99%

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Processing of smart meters data for peak load estimation of consumers

Grigoraș

Scarlatache

2015

2015 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE)

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The paper presents a Mining Data peak load estimation of household consum uses an unsupervised learning method (clu the polynomial regression. With K-means cl the consumption categories of household determined. The input data for the consume consumption categories (monthly energy cons loads) are obtained from processing the ty provided by Smart Meters. For each consu polynomial regression model is built for peak household consumers equipped with cla obtained results demonstrate that the metho with the success in peak load estimation customers from distribution systems when i poor (based on the data provided by classic m

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Section: Description Of Proposed Methodologymentioning

confidence: 99%

Section: Data Mining For Larmentioning

confidence: 99%

Section: Data Mining For Larmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Processing of smart meters data for peak load estimation of consumers

Grigoraș

Scarlatache

2015

2015 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE)

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“…Additionally, a wide range of services is emerging, such as consuming profiles, load control, remote switching of home devices, and remote consumption monitoring, among others. For instance, in [21] the data from domestic energy consumption is used together with an algorithm to establish categories of energy consumers, while [22] contains an analysis of generated energy savings after the installation of SMs in Korean homes. Nevertheless, some energy managers are expected to play an important role in the progressive deployment of the triple play (confluence of audio, video, and Internet access).…”

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confidence: 99%

State of the Art and Trends Review of Smart Metering in Electricity Grids

Uribe-Pérez¹,

et al. 2016

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Climate change, awareness of energy efficiency, new trends in electricity markets, the obsolescence of the actual electricity model, and the gradual conversion of consumers to prosumer profiles are the main agents of progressive change in electricity systems towards the Smart Grid paradigm. The introduction of multiple distributed generation and storage resources, with a strong involvement of renewable energies, exposes the necessity of advanced metering or Smart Metering systems, able to manage and control those distributed resources. Due to the heterogeneity of the Smart Metering systems and the specific features of each grid, it is easy to find in the related literature a wide range of solutions with different features. This work describes the key elements in a Smart Metering system and compiles the most employed technologies and standards as well as their main features. Since Smart Metering systems can perform jointly with other activities, these growing initiatives are also addressed. Finally, a revision of the main trends in Smart Metering uses and deployments worldwide is included.

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