<p>The integration of renewable energy sources can significantly reduce carbon footprints; however, it also introduces challenges in maintaining uninterrupted energy services, thereby demanding advancements in technology. The increasing availability of energy consumption data from buildings has opened up the possibility of implementing data-driven methods to improve Demand Side Management (DSM) operations in energy utilities. In this context, the current paper proposes a novel indexing approach for buildings and appliances using a multiple network. The multiple network is constructed from a time series dataset, where each network represents a different time granularity. A quasi-clique-based clustering technique is applied to the multiple network to group similar subsequences based on recurring patterns. From the resulting clusters, an indexing measure is derived to quantify the stability of buildings and appliances. This paper presents a detailed analysis of changes in the stability index across various granular levels. A comparative study with existing state-of-the-art clustering techniques establishes the superiority of the proposed method.</p>