Detection and diagnosis of anomalous energy consumption patterns in buildings through a data analytics based approach: the case of Politecnico di Torino

In recent years, Smart Metering Infrastructure (SMI) has enabled the collection of huge amounts of building-related data. However, very often, only time series of a few aggregated variables associated with building energy consumption are available. Therefore, it becomes necessary to extract from meter level data as much information as possible in order to optimize building energy management, by reducing losses due to inefficiencies or anomalous behaviour of sub-systems and equipment. This paper proposes an innovative top-down Fault Detection and Diagnosis (FDD) methodology able to automatically detect at whole build- ing meter-level anomalous energy consumption and then diagnosticate which sub-load could be responsible. The process consists of a multi-step procedure combining various data mining techniques. An evolutionary classification tree is firstly implemented to discover frequent and infrequent daily aggregated energy patterns opportunely abstracted through a symbolic approximation pro- cess. Then a post-mining analysis based on Association Rule Mining (ARM) is performed to discover the main sub-loads affecting the detected anomalous energy patterns. The methodology is tested on metering data related to the electrical load of a transformer substation of a university campus, leading to the development of a tool useful to support the energy management with a complete characterization and diagnosis of energy demand at a daily scale.