Disturbance Magnitude Estimation using Artificial Neural Network Method

Hartono, Joko; Pramana, Putu Agus Aditya; Tambunan, Handrea Bernando; Munir, Buyung Sofiarto

doi:10.1109/iceei47359.2019.8988903

Cited by 3 publications

(1 citation statement)

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“…This algorithm is an analytical method in data analysis and also data mining to learn the information by grouping the data. Data analysis with high dimensional data such as images, videos, text, measurement and more now widely used in many fields such as communication, biology, computer science, economic, epidemiology, psychology, business, and also engineering fields such as electrical power utilities [1]- [4]. In the present and future modern power system, the information from big data analytics has a great potential to be exploited for example from the demand side.…”

Section: Introductionmentioning

confidence: 99%

Electrical Peak Load Clustering Analysis Using K-Means Algorithm and Silhouette Coefficient

Tambunan¹,

Barus²,

Hartono³

et al. 2020

2020 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP)

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Nowadays, data analysis widely used in many fields especially in engineering. Clustering is one of data analysis methods to organize the amount of data into groups with similarity characteristics. One powerful analysis method to learn information by grouping data is clustering algorithms. The clustering advantages for electrical power utilities is to learn load behavior and provide information for power plant operation and also generation cost. In this paper, a simulation concept is proposed for analysis of peak load data by K-means clustering algorithm based on historical dataset. The results show electrical peak loads clustering by K-means algorithm are optimum classified into three clusters. This cluster evaluated by silhouette scores which high, intermediate, and low load level interpretation. One cluster has centroid during January, June, and July are relatively lower than another cluster caused by Indonesia national holiday. This concept also evaluates the load level affected by Covid-19 pandemic condition.

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Section: Introductionmentioning

confidence: 99%