British Gas Trading Limited

Stephen Wood

Miroslav Hamouz

Oliver Parson

Jim Anning

Alexandros Leontitsis

Adi Andrei

A computer-implemented method of generating predicted energy consumption data for a first energy consumer based on energy consumption data of a plurality of further energy consumers is disclosed. Prediction is based on a set of training samples, each training sample comprising predictor attribute data and energy consumption data for a respective one of the further energy consumers, as well as one or more predictor attribute values for the first energy consumer. A predictive model is generated based on the training samples, the predictive model defining a relationship between values of predictor attributes and energy consumption data of the training samples. Predicted energy consumption data for the first energy consumer is then determined using the predictive model and the predictor attribute values. A method of clustering energy consumption profiles is also disclosed.

System for energy consumption prediction

In one aspect, there is described a method having: receiving energy consumption data from one or more electricity and/or gas meters associated with a property having one or more devices having larger devices and/or smaller devices; receiving context information data about the property; retrospectively determining, in the received energy consumption data, one or more variations indicative of consumption of the devices; identifying one or more events associated with the devices, based on the determined variations; classifying the identified events into predetermined sub-categories associated with the devices, based on the energy consumption data and the context information data; and associating a proportion of the received energy consumption data to respective predetermined categories associated with the devices, based on the classification in the sub-categories.

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London, United Kingdom

Stephen Wood