Determination of Typical Electricity Load Profile by Using Double Clustering of Fuzzy C-Means and Hierarchical Method

Anuar, Nurhilyana; Baharin, N. K. K.; Nizam, Nusaiba; Fadzilah, A.N.; Nazri, Sharifah; Lip, Norliana Mohd

doi:10.1109/icsgrc53186.2021.9515295

Cited by 1 publication

(2 citation statements)

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“…Micro-spatial analysis is an area analysis based on land use simulation modeling, which begins by dividing the service area and electricity utility into a set of small areas [3], [6]. The size of the grid varies depending on the availability of data and the variables used.…”

Section: Methods 21 Microspatial Analysismentioning

confidence: 99%

“…Usually, many loads forecasting done has not considered to use load profile characterization (LPC) as its integral reference to get more accurate prediction result. Whereas the more precise load forecast can result in the better quality of network development planning, the better design of energy conservation, the increasing efficiency of the power system due to the high quality of load management strategies, precise customer classification, as well as precise marketing policies and electricity tariff [2], [3]. However, the usual LPC result is based on macro and sectoral, making it difficult to map and determine the existence of load centers [4].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization

Senen

Putri

Jamian

et al. 2023

IJEECS

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As the rising of electricity demand, electricity load profile characterization (ELPC) is the integral aspect in planning, operating system, and distribution network development. The approach in the existing ELPC is still relatively macro in nature and does not involve other aspects outside the electricity variable, so the results tend to be biased for areas experiencing rapid land use changes. Therefore, this paper proposes an ELPC approach based on micro-spatial. Microspatial analysis is done by dividing area in the form of the smallest grids involving various electrical, demographic, geographic and socio-economic variables, which are then grouped using adaptive clustering based on fuzzy C-means (FCM). The adaptive clustering algorithm is proven to be able to determine the degree of membership of each grid data against each cluster with the ability to determine the number of clusters automatically according to the attribute data provided. The ELPC results which consist of 5 clusters are then analyzed using descriptive statistic, plotted, and mapped to obtain more accurate and realistic load characteristics in accordance with the pattern and geographical conditions of the region, so that the results can be used as a reference in load forecasting, network development, and distributed generation (DG) integration.

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Section: Methods 21 Microspatial Analysismentioning

confidence: 99%