Residential appliance clustering based on their inherent characteristics for optimal use based K-means and hierarchical clustering method

Subject Areas : Management

Shima  Simsar ¹ (Department of Information Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Mahmood  Alborzi ² (Department of Information Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Ali  Rajabzadeh Ghatari ³ (Department of Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran)
Ali  Yazdian Varjani ⁴ (Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran)

Received: 2022-12-31 Accepted : 2023-05-09 Published : 2023-06-01

Keywords: Demand Response, k-means clustering, Hierarchical Clustering, Appliance,

Abstract :

With global warming and energy shortages, smart grids have become a significant issue in the power grid. Demand response is one of the basic factors of smart grids. To enhance the efficiency of demand response, an intelligent home appliance control system is essential, which prioritizes the start-up of electrical appliances according to the necessity of use and efficiency. To properly manage the demand response, utilities use different signals such as price. One of the pricing methods that can be considered is different pricing for electrical appliance clusters. In this article, appliances are clustered by the K-means and hierarchical clustering based on the characteristics of the appliances themselves, such as the appliances’ extent of consumption, the type of use of home appliances, how home appliances work, the ability to change the working conditions of home appliances, home appliances usage time, etc. It seems that the K-means clustering method outperforms the hierarchical method in this issue, due to its lower value of DB coefficient. In this method, home appliances were classified into three clusters. The silhouette coefficient was developed as a measure of the K-means clustering model performance, where the average silhouette coefficient of 0.6 indicates the satisfactory value of the model. Based on the results, it was found that the proposed clustering method can rationally classify different types of home appliances by selecting the appropriate characteristics since the appliances in a cluster are very similar to each other and can help users understand the operating conditions of the appliances.

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