Energy consumption clustering using machine learning: K-means approach

Nowadays, the accurate analysis of energy consumption has become vital for the development of efficient energy projects as well as, for demonstrating the consumptive behavior of the energy consumers in the system. The importance of this analysis comes from many reasons, one of them is that it leads to a better understanding of the system components. This paper presents a clustering algorithm for residential energy consumption using the K-Means algorithm in two different approaches. The dataset utilized in this article contains energy consumption features selected from 25 houses over a period of two years. Firstly, data cleaning has been used to remove and eliminate the inconsistent data, secondly the Elbow method has been applied to determine the optimal number of clusters before using the K-means approach for the purpose of clustering. In K-means, the data have been clustered into two different approaches. The first one is clustering the daily mean consumption in each season in each year. The second one is clustering the monthly mean consumption over the two years. Finally, data visualization has been applied in order to present the result of our proposed method. The paper finds that the households have different consumption behaviors in different seasons, days, and months and that it is due to the change of the average temperature in each season as well as the different appliances and consumptive patters of each house. The results are representative and match the aim of the paper. Further, they are significant for the further development of the energy system and efficient for tracking the consumption of the houses. Finally, the results of this paper are going to be used after running the algorithm again with a different number of clusters to compare the results and find new insights in the data that might affect the decision.