Vincenzo Viggiano
Development of a data-driven methodology for the detection and diagnosis of meter-level energy anomalies in buildings.
Rel. Alfonso Capozzoli, Marco Savino Piscitelli, Rocco Giudice. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Energetica E Nucleare, 2025
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Abstract
In 2023, electricity consumption in Italy reached 287.4 TWh, with the building sector alone accounting for approximately 40% of this total. This significant share underscores the pressing need for effective energy monitoring and management strategies within buildings to support both operational efficiency and long-term sustainability goals. In this context, Energy Management and Information Systems (EMIS) have emerged as essential tools, offering integrated platforms for real-time data collection, performance benchmarking, and decision support. Among the various functionalities provided by EMIS, Anomaly Detection and Diagnosis (ADD) is particularly valuable. It enables the identification of abnormal consumption patterns and potential system inefficiencies in energy consumption, serving as a critical leverage for energy optimization and supporting decision making.
However, traditional data-driven ADD approaches, often based solely on statistical or machine learning methods, which tend to oversimplify or ignore the physical-operational context of building systems
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