Smart meter data taxonomy for demand side management in smart grids

Khan, Zafar A.; Jayaweera, Dilan; Gunduz, Hasan

doi:10.1109/pmaps.2016.7764143

Cited by 8 publications

(26 citation statements)

References 12 publications

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“…In Fig. 1, it can be noted that a substantial amount of loads is concentrated at the region below 10 kWh [18].…”

Section: The Approachmentioning

confidence: 90%

“…For example, in this case from Fig. 1 due to higher energy concentration in the region below 10 kWh, the load below 10 kWh can easily be perceived as one cluster despite having large variations within the region [18].…”

Section: The Approachmentioning

confidence: 92%

“…As given by [18] owing to the applicability, proven robustness, higher processing speed, and proven efficiency, a The proposed approach for load profiling involves many processing stages including data clustering, pattern extraction and curve smoothing. Initially in the k-means clustering, the 'ISSDA CER' dataset [19] which comprises of smart meter data from more than 5,000 homes and businesses in Ireland with a resolution of 30 minutes is populated.…”

Section: The Approachmentioning

confidence: 99%

“…2. The approach consists of two parts, part (A) was developed in our previous work [18] and part (B) develops an approach to use the resultant load profiles for MC simulation applications. In this part (A) of the approach, after the data pre-processing, the smart meter data is subjected to kmeans clustering as given in steps i to vi.…”

Section: The Approachmentioning

confidence: 99%

“…If any data point member changes cluster membership, repeat the procedure from iii until no change in cluster membership occurs. Upon completion of the process, the resultant clusters are evaluated for their fitness for the extraction of typical load profiles [18]. If the intra-cluster similarities of energy consumption patterns are such that only clear energy consumption patterns with reasonably small variations from each other are allocated the same cluster, the resultant cluster is considered as decisive cluster for load profile extraction.…”

Section: Imentioning

confidence: 99%

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Approach for smart meter load profiling in Monte Carlo simulation applications

Khan

Jayaweera

2017

IET Generation, Transmission & Distribution

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Smart grids introduce new technological elements into power systems which take prevalent challenges to a new level by shaping parameters of power systems towards a complex regime of uncertainties. Rapid proliferation of advanced metering infrastructure (AMI) and integration of renewable energy sources (RES) in smart grids increase system-wide complexities. This paper proposes an innovative approach to classify the energy consumptions of smart meter customers with typical profiles by processing with multi-layered clustering of energy consumption data of smart consumers extracted from the AMI. There are two stages for the approach of which the first stage analyses the data for intra-cluster similarity of energy consumption patterns and in case the patterns do not have a high intra-cluster similarity, they are fed back for re-clustering with multilayered clustering process until the clearly identifiable energy patterns with high intra-cluster similarity is obtained. The second stage linearizes the complex energy patterns using interpolant and curve fitting techniques until stabilised profiles are obtained. The paper also proposes a methodology for smart meter load modelling for Monte Carlo (MC) simulation applications to reduce the computing time compared with traditional alternatives. The paper validates the robustness of the approach and provides the corroboration of the method for MC simulation applications in a smart grid environment.

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“…In Fig. 1, it can be noted that a substantial amount of loads is concentrated at the region below 10 kWh [18].…”

Section: The Approachmentioning

confidence: 90%

Section: The Approachmentioning

confidence: 92%

Section: The Approachmentioning

confidence: 99%

Section: The Approachmentioning

confidence: 99%

Section: Imentioning

confidence: 99%

See 3 more Smart Citations

Approach for smart meter load profiling in Monte Carlo simulation applications

Khan

Jayaweera

2017

IET Generation, Transmission & Distribution

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Managing Smart City Power Network by Shifting Electricity Consumers Demand

Silva¹,

Faria²,

Vale

2021

Sustainable Smart Cities and Territories

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Demand Response (DR) concept, introduced by the Smart Grid paradigm, is presented as one of the main solutions to mitigate the effects of the intermittency of Distributed Generation sources in the network. With this, the consumer's role in the energy market is empowered and their flexibility is crucial. The technology advances allow bidirectional communication providing signals to the active consumers, given by the entity manager, participating in DR events to alleviate problems in the grid. The authors proposed a methodology, resorting to load shifting, to lessen voltage limit violations in some points of the grid. After the detention, the DR event is triggered and the small resources are scheduled, requesting a reduction to the active consumers. The authors believe that uncertainty in the response must be considered so, the willingness and availability from the active community are contemplated on the problem. The results show the necessity of pondering consumer behavior.

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