A method of detecting conditions indicative of energy meter tampering, meter faults or energy loss (e.g. due to energy theft) is disclosed. The method includes receiving energy consumption data from an energy meter indicating consumption of energy at a location served by the energy meter. Event data is also received from the meter comprising one or more events generated by the energy meter. The consumption data is analysed to detect a predetermined consumption condition. The event data is analysed to detect a predetermined event or event pattern in the event data. An alert condition is generated in response to detecting both the consumption condition and the event or event pattern.
A method of detecting an operating state of a process, system or machine based on sensor signals from a plurality of sensors is disclosed. The method comprises receiving sensor data, the sensor data based on sensor signals from the plurality of sensors and providing the sensor data as input to a neural network. The neural network comprises an encoder sub-network arranged to receive the sensor data as input and to generate a context vector based on the sensor data; and a decoder sub-network arranged to receive the context vector as input and to regenerate sensor data corresponding to at least a subset of the sensors based on the context vector. The method comprises comparing the context vector to at least one context vector classification; detecting an operating state in dependence on the comparison; and outputting a notification indicating the detected operating state.
A method and apparatus for analysing utility consumption at a utility supply location is described. The method comprises the steps of: receiving utility consumption data corresponding to utility consumption at the utility supply location over a time period to be analysed; generating a recurring consumption model indicative of repeating consumption patterns in the utility consumption data; identifying divergences between the utility consumption data and the recurring consumption model; computing a diagnostic measure indicative of irregular consumption based on the identified divergences; and outputting the diagnostic measure. The diagnostic measure may be used to identify flexibility or irregularities in consumption and/or to control supply of the utility. The utility may be e.g. electricity, gas or water.