A Hybrid Machine Learning Framework for Prioritizing Battery Energy Storage System Installations for Existing CCTV: A Case Study in Latkrabang, Bangkok, Thailand

This research develops a decision support system for prioritizing Battery Energy Storage System (BESS) installations at existing closed-circuit television (CCTV) camera locations experiencing power interruptions in Latkrabang subdistrict. The methodology integrates nine validated features: outage frequency, downtime duration, maximum outage duration, Net Present Value (NPV), combined ROI, outage impact score, annual BESS cost, combined risk score, and UPS installation cost, derived from historical power outage records (2020–2023) and engineering economics calculations. An unsupervised K-means clustering algorithm, validated through silhouette analysis and the elbow method, categorizes installations into five risk levels, namely critical, very high, high, medium, and low, addressing the absence of predefined ground truth labels. Subsequently, Support Vector Machine (SVM) with hyperparameter optimization classifies priority installations using stratified train-test splitting (80:20). The model was initially developed and validated using 82 CCTV cameras from Lamphla Tiew subdistrict (the pilot area). The validated model was then successfully applied to 101 CCTV cameras in Latkrabang subdistrict (the target area), identifying 27 critical installation points requiring immediate BESS deployment. The weighted recommendation system balances data-driven clustering with scoring: NPV (35%), outage impact (25%), combined ROI (20%), maximum outage duration (10%), and BESS cost efficiency (10%).