biddca22/morocco-smart-meter-analytics

Python-based analysis of high-resolution smart meter data across four Moroccan cities. Applying time series clustering, dimensionality reduction, change-point detection, and anomaly detection to identify consumption profiles and structural shifts. Includes data preprocessing and visualisation for energy demand insights. # Finding Patterns and Anomalies in Real Data ### Abstract This project analyses high-frequency electricity consumption data from four Moroccan cities to discover typical demand patterns, structural variations, and anomalies. Through unsupervised learning, dimensionality reduction, change-point detection, and ensemble anomaly methods, different residential, commercial, and industrial load profiles are identified. The results show pronounced spatial and temporal heterogeneity, providing valuable information for energy planning and network performance. ## 1 Introduction Understanding electricity consumption behaviour is an important step in order to improve energy planning, infrastructure management, and demand forecasting. Morocco, for instance, has seen rapid urbanisation along with the presence of housing, businesses, and industrial activities, result in electricity demand becoming more varied and unpredictable across cities and zones. These patterns fluctuate not only by location—between different areas within the same city—but also temporally, as energy usage evolves in reaction to seasonal effects, socioeconomic activity, and upgrades in infrastructure. Recognising this variability requires data-driven methods that can uncover relevant structure from large-scale, granular electricity consumption data. The main goal of this project is to characterise electricity consumption habits in both domestic and industrial zones throughout Morocco. In particular, this work aims to determine if distinct daily load profiles can be identified, such as continuous industrial demand or household patterns marked by evening peaks. It also explores geographic inconsistency by assessing whether zones within the same city display comparable consumption behaviour or form distinct subgroups. Additionally, this study intends to capture time-related trends by spotting meaningful changes in how electricity is consumed over time, including seasonal variations or sudden changes that may …