Abstract: Estimator mechanisms for automated computer causal effect estimation are provided. An input dataset is received that includes an initial set of covariate data. An estimation of the relevance of covariates in the initial set is performed where relevance is to one or more causal effect relationships between a given at least one action and an outcome. Based on results of the execution of the estimation, a subset of the initial set of covariates is determined that are covariates relevant to one or more causal effect relationships. A modified dataset, comprising the subset of relevant covariates and at least a portion of the input dataset is generated. The modified dataset is input to a causal effect estimator that processes the modified dataset to generate causal effect relationship estimates for specifying causal effects between the given set of actions and the outcome.

Abstract: A system may predict costs for a set of members by building and using a predictive pipeline. The pipeline may be built using a set of historical data for training members. A set of member-level features can be identified by performing empirical testing on the set of historical data. The trained configurable predictive pipeline can generate a set of predictive data for each member, using historical test data for a set of testing members. The system can then generate a predictive report for each set of predictive data.

Abstract: A system, method and program product for cost attribution using multiple factors, in which transactional data sets from two or more time periods are analyzed based on multiple potential factors in the data sets that can be correlated to cost. The potential factors are systematically analyzed to identify a set of cost factors and compute the cost impact for each cost factor. An infrastructure is disclosed having a data selection system; a potential factors system; a factor hierarchy system; an actionability class system; a factor processing system and a cost factor reporting system for providing the cost impact of the set of cost factors based on analysis of the transactional data sets.

Abstract: System and method of solving, in a single-period, an optimal dispatching problem for a network of energy generators connected via multiple transmission lines, where it is sought to find the lowest operational cost of dispatching of various energy sources to satisfy demand. The model includes traditional thermal resources and renewable energy resources available generation capabilities within the grid. The method considers demand reduction as a virtual generation source that can be dispatched quickly to hedge against the risk of unforeseen shortfall in supply. Demand reduction is dispatched in response to incentive signals sent to consumers. The control options of the optimization model consist of the dispatching order and dispatching amount energy units at generators together with the rebate signals sent to end-users at each node of the network under a demand response policy. Numerical experiments based on an analysis of representative data illustrate the effectiveness of demand response as a hedging option.

Abstract: System and method of solving, in a single-period, an optimal dispatching problem for a network of energy generators connected via multiple transmission lines, where it is sought to find the lowest operational cost of dispatching of various energy sources to satisfy demand. The model includes traditional thermal resources and renewable energy resources available generation capabilities within the grid. The method considers demand reduction as a virtual generation source that can be dispatched quickly to hedge against the risk of unforeseen shortfall in supply. Demand reduction is dispatched in response to incentive signals sent to consumers. The control options of the optimization model consist of the dispatching order and dispatching amount energy units at generators together with the rebate signals sent to end-users at each node of the network under a demand response policy. Numerical experiments based on an analysis of representative data illustrate the effectiveness of demand response as a hedging option.

Abstract: A method, apparatus and computer program product for determining lowest cost aggregate energy demand reduction at multiple network levels such as distribution and feeder networks. An algorithm for an optimal incentive mechanism offered to energy customers (e.g. of a utility power entity) that accounts for heterogeneous customer flexibility in load reduction, with the demand response realized via the utility's rebate signal and, accounts for temporal aspects of demand shift in response for rebates. A mathematical formulation of a cost minimization problem is solved to provide incentives for customers to reduce their demand. A gradient descent algorithm is used to solve for the optimal incentives customized for individual end users.