Abstract: A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

Abstract: A streams platform is used. Multiple streams of electricity usage data are received, each from an electrical meter providing periodic updates to electrical usage for devices connected to the electrical meter. Weather information is received corresponding to locations where the electrical meters are. Real-time predictive modeling of electricity demand is performed based on the received multiple streams of electricity usage data and the received weather information, at least by performing: updating a state space model for electrical load curves using the usage data from the streams and the weather, wherein the updating uses current load observations for the multiple streams for a current time period; and creating forecast(s) for the electricity demand. The forecast(s) of the electricity demand are output. Appliance-level predictions may be made and used, and substitution effects and load management functions may be performed.

Abstract: A streams platform is used. Multiple streams of electricity usage data are received, each from an electrical meter providing periodic updates to electrical usage for devices connected to the electrical meter. Weather information is received corresponding to locations where the electrical meters are. Real-time predictive modeling of electricity demand is performed based on the received multiple streams of electricity usage data and the received weather information, at least by performing: updating a state space model for electrical load curves using the usage data from the streams and the weather, wherein the updating uses current load observations for the multiple streams for a current time period; and creating forecast(s) for the electricity demand. The forecast(s) of the electricity demand are output. Appliance-level predictions may be made and used, and substitution effects and load management functions may be performed.

Abstract: A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

Abstract: A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

Abstract: A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

Abstract: Systems and methods for failure prediction and maintenance planning are provided. A system for failure prediction and maintenance planning, comprises a statistical modeling module comprising a periodic impact evaluation module capable of identifying periodic effects on the failure risk, a balance equation systems module capable of constructing balance equations with respect to phases of failure times, and an initial phase estimation module capable of estimating an unknown initial phase, wherein one or more of the modules are implemented on a computer system comprising a memory and at least one processor coupled to the memory.

Abstract: Systems and methods for failure prediction and maintenance planning are provided. A system for failure prediction and maintenance planning, comprises a statistical modeling module comprising a periodic impact evaluation module capable of identifying periodic effects on the failure risk, a balance equation systems module capable of constructing balance equations with respect to phases of failure times, and an initial phase estimation module capable of estimating an unknown initial phase, wherein one or more of the modules are implemented on a computer system comprising a memory and at least one processor coupled to the memory.

Abstract: Systems and methods for failure prediction and maintenance planning are provided. A system for failure prediction and maintenance planning, comprises a statistical modeling module comprising a periodic impact evaluation module capable of identifying periodic effects on the failure risk, a balance equation systems module capable of constructing balance equations with respect to phases of failure times, and an initial phase estimation module capable of estimating an unknown initial phase, wherein one or more of the modules are implemented on a computer system comprising a memory and at least one processor coupled to the memory.

Abstract: Systems and methods for failure prediction and maintenance planning are provided. A system for failure prediction and maintenance planning, comprises a statistical modeling module comprising a periodic impact evaluation module capable of identifying periodic effects on the failure risk, a balance equation systems module capable of constructing balance equations with respect to phases of failure times, and an initial phase estimation module capable of estimating an unknown initial phase, wherein one or more of the modules are implemented on a computer system comprising a memory and at least one processor coupled to the memory.

Abstract: A method and system for forecasting demand for order configurations are provided. The method and system, in one aspect, expresses attach rates within a family of n options as a set of n positive numbers that sum to 1. By applying suitable transformations to the attach rates, they are modeled as a random vector in (n?1)-dimensional Euclidean space. The distribution of the transformed attach rates are modeled as a distribution family specified by a location vector and a dispersion matrix. The dispersion matrix is simplified, for example, using historical data or expert judgment or both to identify option families that have dependent demand. Simplifying may also include expressing dependence between options by a simple model that involves few parameters. Location vector is estimated by computing point forecasts of transformed attach rates. The parameters of the dispersion matrix are estimated by calibration on historical data, using the dispersion of the errors in historical point forecasts.

Abstract: A method and system for estimating a magnitude of extremely rare events upon receiving a complete data sample and a specific exceedance probability are described. A distribution is chosen for a complete data sample. An optimal subsample fitted to the distribution is obtained. The optimal subsample is a largest acceptable subsample. A subsample is considered as an acceptable subsample when a goodness-of-fit test on the subsample is satisfactory (i.e., higher than a predetermined threshold). In addition, if a tail measure of an acceptable subsample lies outside a confidence interval of any smaller acceptable subsample, the acceptable subsample is considered as an unacceptable. Based on the optimal subsample and an inputted exceedance probability, a quantile estimate is computed, e.g., by executing an inverse of a cumulative distribution function of generalized Pareto distribution.

Abstract: A method and system for estimating a magnitude of extremely rare events upon receiving a complete data sample and a specific exceedance probability are described. A distribution is chosen for a complete data sample. An optimal subsample fitted to the distribution is obtained. The optimal subsample is a largest acceptable subsample. A subsample is considered as an acceptable subsample when a goodness-of-fit test on the subsample is satisfactory (i.e., higher than a predetermined threshold). In addition, if a tail measure of an acceptable subsample lies outside a confidence interval of any smaller acceptable subsample, the acceptable subsample is considered as an unacceptable. Based on the optimal subsample and an inputted exceedance probability, a quantile estimate is computed, e.g., by executing an inverse of a cumulative distribution function of generalized Pareto distribution.

Abstract: A new method is used to model the class probability from data that is based on a novel multiplicative adjustment of the class probability by a plurality of items of evidence induced from training data. The optimal adjustment factors from each item of evidence can be determined by several techniques, a preferred embodiment thereof being the method of maximum likelihood. The evidence induced from the data can be any function of the feature variables, the simplest of which are the individual feature variables themselves. The adjustment factor of an item of evidence Ej is given by the ratio of the conditional probability P(C|Ej) of the class C given Ej to the prior class probability P(C), exponentiated by a parameter aj. The method provides a new and useful way to aggregate probabilistic evidence so that the final model output exhibits a low error rate for classification, and also gives a superior lift curve when distinguishing between any one class and the remaining classes.

Abstract: A method for demand planning of products. The method comprises the steps of constructing a configure-to-order operation/multiple building block environment; and, forecasting the demand of the building blocks within this environment for establishing an efficient supply chain management.

Abstract: A new method is used to model the class probability from data that is based on a novel multiplicative adjustment of the class probability by a plurality of items of evidence induced from training data. The optimal adjustment factors from each item of evidence can be determined by several techniques, a preferred embodiment thereof being the method of maximum likelihood. The evidence induced from the data can be any function of the feature variables, the simplest of which are the individual feature variables themselves. The adjustment factor of an item of evidence E1 is given by the ratio of the conditional probability P(C|E1) of the class C given E1 to the prior class probability P(C), exponentiated by a parameter a1. The method provides a new and useful way to aggregate probabilistic evidence so that the final model Output exhibits a low error rate for classification, and also gives a superior lift curve when distinguishing between any one class and the remaining classes.