Electricity load classification using K-means clustering algorithm

Nuchprayoon, Somboon

doi:10.1049/cp.2014.1061

Cited by 9 publications

(9 citation statements)

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“…Cluster centers by day of the electric load data was then determined using K-means clustering method. K-means clustering is a partitional clustering method that attempts to find the number of clusters (k), which are represented by centroids [6], [9]. Cluster parameters such as number of clusters (k) and maximum number of iterations (i) were then determined.…”

Section: A Load Data Preparation and K-means Clusteringmentioning

confidence: 99%

“…Choosing the number of k clusters has no agreed upon solution, thus finding the number of k clusters varies on the number of clusters desired. Three basic steps in K-means clustering were then conducted [6], [7], [9]. Firstly, data items were partitioned into I initial cluster, where data consists of data points from x 1 , x 2 ..,x n and each point is assigned to the nearest centroid.…”

Section: A Load Data Preparation and K-means Clusteringmentioning

confidence: 99%

“…Furthermore, having knowledge of the peak energy usage will help the electric utility plan for the extension of existing facilities and installation of new power plants to reliably meet consumer demand [5]. K-means clustering technique and Artificial Neural Networks (ANN) model are suggested as suitable approach in clustering and forecasting load profile that can determine base, intermediate and peak loads [2], [3], [6], [7]. K-means is a widely used unsupervised learning algorithm that solves clustering problems or data categorizing.…”

Section: Introductionmentioning

confidence: 99%

“…K-means is a widely used unsupervised learning algorithm that solves clustering problems or data categorizing. Moreover, it follows the simple and easy way in classifying datasets into assumed k clusters [6], [7]. While ANN is a mathematical model that has powerful classification and gained a significant performance in most researches mainly load forecasting due to its flexibility and its generality.…”

Section: Introductionmentioning

confidence: 99%

“…While ANN is a mathematical model that has powerful classification and gained a significant performance in most researches mainly load forecasting due to its flexibility and its generality. Being one of the popular machine learning technique, supervised ANN has been widely used and has been proven to predict promising forecast of electric load but there are still gaps in coming up with a serial process in its usage along with unsupervised K-means [1], [2], [6].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Day-ahead Base, Intermediate, and Peak Load Forecasting using K-Means and Artificial Neural Networks

Velasco¹,

Estoperez²,

Jayson³

et al. 2018

ijacsa

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Industries depend heavily on the capacity and availability of electric power. A typical load curve has three parts, namely, base, intermediate, and peak load. Predicting the three (3) system loads accurately in a power system will help power utilities ensure the availability of the supply and to avoid the risk for over-or under-utilization of generation, transmission, and distribution facilities. The goal of this research is to create a suitable model for day-ahead base, intermediate and peak load forecasting of the electric load data provided by a power utility company. This paper presents an approach in predicting the three (3) system loads using K-means clustering and artificial neural networks (ANN). The power utility's load data was clustered using K-means to determine the daily base, intermediate and peak loads that were then fed into an ANN model that utilized Quick Propagation training algorithm and Gaussian activation function. It was found out that the implemented ANN model generated 2.2%, 1.84%, and 1.4% as the lowest MAPE for base, intermediate, and peak loads, respectively, with highest MAPE below the accepted standard error rate of 5%. The results of this study clearly suggest that with the proper method of data preparation, clustering, and model implementation, ANN can be a viable solution in forecasting the day-ahead base, intermediate, and peak load demand of a power utility.

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Section: A Load Data Preparation and K-means Clusteringmentioning

confidence: 99%