A Data-Driven analysis of electrical energy use in Lombardy: Clustering and Statistical Analysis

Analyzing electrical consumption data is crucial for optimizing energy management and integrating renewables. Existing approaches often lack precision in categorizing consumption patterns across diverse buildings. This study applies unsupervised clustering, along with statistical methods, to office buildings quarter-hourly energy consumption data from Lombardy, Italy.

The results provide sector-specific annual consumption patterns with hourly time-step, improving demand-side management and enabling better Renewable Energy Community (REC) planning. This research contributes by refining analytical methodologies for electrical consumption data, supporting urban energy modeling, and enhancing energy policy decisions toward a more efficient and sustainable power system.