Abstract: Methods, systems, and computer-readable storage media for automatically providing a predictive model for an asset made up of multiple sub-assets with actions including receiving asset data including data values associated with the asset and at least one of sub-asset of the multiple assets, providing, by the one or more processors, a set of features based on the asset data, and executing an iterative feature selection and supervised learning process, including, for each iteration: selecting a sub-set of features from the set of features, performing supervised learning over the sub-set of features to provide a predictive model, and determining an accuracy of the predictive model, the iterations are performed until the accuracy of the predictive model exceeds a threshold accuracy.

Abstract: A method includes receiving, at computing system, sensor data from a machine, preparing the sensor data for use by data mining algorithms, generating an analysis table based on the prepared sensor data, the analysis table including information and data for a plurality of instances for the machine, and using the information and data included in the analysis table to predict a failure of the machine.

Abstract: A benchmarking computing application is configured to create a benchmark run for a segment of customer profiles. Customer profiles from the segment include a parameter, termed an adjustment parameter, that may affect energy consumption by the associated customer. The results of the benchmark run include energy consumption values for individual customer profiles in the segment, an average energy consumption value for the entire segment of customer profiles, and also adjustment energy consumption value. The adjustment energy consumption value reflects the portion of energy consumption by a particular customer attributed to the difference between the average value of the adjustment parameter for the segment and the value of the adjustment parameter for the particular customer profile.

Abstract: A method and system to determine customer segmentation based on energy consumption patterns is provided. An example system includes a communications module, a clustering module, and a matching module. The communications module obtains energy consumption data in the form of a plurality of value days. The clustering module groups the value days associated with a certain period of time into a set of clusters. The matching module identifies a customer profile as associated with a cluster from the set of clusters based on results of examining value days associated with the customer profile.

Abstract: A benchmarking computing application is configured to create a benchmark run for a segment of customer profiles. Customer profiles from the segment include a parameter, termed an adjustment parameter, that may affect energy consumption by the associated customer. The results of the benchmark run include energy consumption values for individual customer profiles in the segment, an average energy consumption value for the entire segment of customer profiles, and also adjustment energy consumption value. The adjustment energy consumption value reflects the portion of energy consumption by a particular customer attributed to the difference between the average value of the adjustment parameter for the segment and the value of the adjustment parameter for the particular customer profile.