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.