Smart Energy Monitoring for Sustainable Campuses: A Hybrid Anomaly Detection Approach Based on Prophet and Isolation Forest

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Abstract
The transition towards sustainable educational campuses requires robust energy management strategies that integrate operational oversight with advanced analytics. This paper presents a campus-scale electricity monitoring system at the University of Algarve, designed to support the institution’s sustainability goals through continuous monitoring, data reliability, portability, and scalability to handle concurrent high-frequency campus-wide telemetry. The system consolidates heterogeneous meters into a unified platform, enabling precise tracking of energy consumption and photovoltaic generation. Beyond operational efficiency, the platform incorporates a data-driven analytical layer featuring short-term forecasting using Prophet, chosen for its computational scalability, and a hybrid anomaly detection scheme combining forecast residuals with Isolation Forest. These capabilities facilitate the early identification of waste and abnormal consumption patterns, directly contributing to energy conservation and carbon footprint reduction. Validated across multiple buildings, the system demonstrates both portability to different energy profiles and high data continuity, reducing the cognitive load on facility managers. By providing a reproducible blueprint for intelligent energy monitoring, this work supports institutions in their pursuit of energy efficiency and sustainable development, aligning operational practices with broader environmental objectives.
Funding Information
  • NOVA Laboratory for Computer Science and Informatics (UID/04516/2025)
  • European Union through the FEDER
  • INTERREG programs (AGERAR+ (0091_AGERAR_PLUS_6_E))
  • INTERREG programs (CEL_RURAL (0081_CEL_RURAL_6_E_V3))

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