  Energy consumption analysis plays a pivotal role in addressing the challenges
of sustainability and resource management. This paper introduces a novel
approach to effectively cluster monthly energy consumption patterns by
integrating two powerful techniques: Self-organizing maps and K-means
clustering. The proposed method aims to exploit the benefits of both of these
algorithms to enhance the accuracy and interpretability of clustering results
for a dataset in which finding patterns is difficult. The main focus of this
study is on a selection of time series energy consumption data from the Smart
meters in London dataset. The data was preprocessed and reduced in
dimensionality to capture essential temporal patterns while retaining their
underlying structures. The SOM algorithm was utilized to extract the central
representatives of the consumption patterns for each one of the houses over the
course of each month, effectively reducing the dimensionality of the dataset
and making it easier for analysis. Subsequently, the obtained SOM centroids
were clustered using K-means, a popular centroid-based clustering technique.
The experimental results demonstrated a significant silhouette score of 66%,
indicating strong intra-cluster cohesion and inter-cluster separation which
confirms the effectiveness of the proposed approach in the clustering task.

Machine Learning;Artificial Intelligence