Abstract: 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.

Abstract: In one aspect, there is provided a method comprising: receiving consumption data comprising readings from one or more utility meters associated with a property comprising one or more devices, the one or more devices comprising one or more devices of interest; determining, in the received consumption data, one or more positive consumption variations indicative of switching on of one or more of the devices and/or one or more negative consumption variations indicative of switching off of one or more of the devices; identifying one or more events associated with the one or more devices, based on the determined variations, by matching one or more positive variations with one or more negative variations; grouping the identified one or more events into one or more blocks, each block corresponding to an occurrence of usage of a device of the property; classifying the one or more blocks into one or more predetermined clusters, the one or more predetermined clusters comprising a respective predetermined cluster associa

Abstract: 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.

Abstract: In one example, there is provided a computer-implemented method for estimating, at a given time, a fluid flow state in a conduit of a property, comprising: obtaining historical data associated with at least one fluid flow state in the conduit, wherein the historical data comprises a history of the probability of the at least one fluid flow state in the conduit; obtaining a probability model for the at least one fluid flow state, wherein a probability for the at least one fluid flow state is a function of the historical data associated with the at least one fluid flow state; obtaining observed data associated with the property, the observed data including a measure representative of fluid flow in the conduit; and estimating a fluid flow state in the conduit of the property at the given time, based on the historical data associated with the at least one fluid flow state, the probability model and the observed data.

Abstract: 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.

Abstract: 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.

Abstract: In one aspect, there is provided a method comprising: receiving consumption data comprising readings from one or more utility meters associated with a property comprising one or more devices, the one or more devices comprising one or more devices of interest; determining, in the received consumption data, one or more positive consumption variations indicative of switching on of one or more of the devices and/or one or more negative consumption variations indicative of switching off of one or more of the devices; identifying one or more events associated with the one or more devices, based on the determined variations, by matching one or more positive variations with one or more negative variations; grouping the identified one or more events into one or more blocks, each block corresponding to an occurrence of usage of a device of the property; classifying the one or more blocks into one or more predetermined clusters, the one or more predetermined clusters comprising a respective predetermined cluster associa