# Intelligent Monitoring Systems for Electric Vehicle Charging
## 4 Intelligent EV Charging Monitoring System
### User Behaviour Analysis  
Our literature review (Section 3) suggests that existing systems often lack detailed behavioral analytics, with most implementing basic usage tracking [3; 24] but few using advanced behavioral modeling [24; 29]. The proposed framework addresses this limitation by using a multi-modal approach combining real-time monitoring with predictive analytics to characterize and predict EV charging patterns through spatiotemporal analysis. Our approach goes beyond traditional temporal analysis [27; 81], by incorporating spatial correlations and contextual factors crucial for accurate behavior prediction. Specifically, we combine real-time monitoring data with historical usage patterns to develop predictive models of charging station utilization. Empirical studies [27; 81; 82] demonstrate that charging behavior exhibits distinct temporal patterns that significantly influence infrastructure optimization. By conducting analysis at both individual and aggregate levels, we may enable greater infrastructure management efficiency [6; 7; 25], through behavioral data integration [24; 28]. Statistical analysis may employ Cox proportional hazards models [83] to quantify factors affecting post-charging occupancy duration [26]. These models have robust handling of time-to-event data and capacity to integrate both static and dynamic covariates [83]. Feature extraction could utilize an adaptive sliding window, with dimensions dynamically optimized based on observed charging patterns [3; 30]. This approach would enable the capture of both short-term post-charging dynamics and extended occupancy trends. The window dimension adaptation algorithm could respond to temporal variations in occupancy patterns [26]. The feature set should encompass multiple temporal dimensions: historical session durations, inter-session intervals, and spatiotemporal occupancy patterns across both target and adjacent charging stations. This represents a feature engineering approach capable of capturing both individual usage patterns and local infrastructure dynamics [24; 27]. The model would incorporate contextual variables, including temporal cycles (daily and weekly) and event-based factors that demonstrate significant influence on parking behavior. System performance targets would likely aim for high accuracy in overstay prediction shortly after charging completion [29]. This accuracy threshold would balance the operational requirement for prompt intervention with practical considerations for user response time. Temporal pattern identification could employ advanced unsupervised learning methodologies [3; 29]. The core algorithm may implement a modified time-series clustering approach utilizing Dynamic Time Warping (DTW) distance metrics [24; 84]. DTW is considered due to its capacity to handle variable-length sequences and inherent robustness to temporal distortions characteristic of real-world charging data [27]. The algorithm could incorporate a novel warping penalty mechanism [26], potentially preventing spurious pattern matches while maintaining sensitivity to genuine behavioral similarities. Pattern classification is expected to employ [PERSON]'s method for hierarchical clustering [28]. This hierarchical approach could reveal both dominant usage patterns and significant deviations from typical behavior. While alternative clustering methods are possible, hierarchical clustering has shown promising capability in identifying nested behavioral patterns and multi-scale temporal structures [7]. The clustering implementation would aim for a silhouette coefficient [85] threshold of 0.8, ensuring robust cluster separation and internal cohesion. This metric represents an established quality benchmark in unsupervised pattern analysis, particularly for behavioral data. We propose to integrate STL (Seasonal and Trend decomposition using Loess) decomposition for multi-scale temporal analysis, enabling the identification of daily, weekly, and monthly usage cycles [32].