Abstract: Pricing a commodity based on a forecast received via remote access over a computer network. The forecast is an amount of the commodity that the user will use over a given time period. The provider supplies the user with an actual amount of the commodity over the given time period and then determines a price for the user for the supplied commodity, in retrospect, after the given time period, for the actual amount of the commodity supplied to the user over the given time period, based on the actual amount of the commodity provided to the user and an accuracy of the forecast of the amount of the commodity received from the user, the accuracy being determined by a difference between said forecast and the actual amount of the commodity supplied to the user over the given time period. The user is then charged to determined price.

Abstract: A joint utility predictor and controller (JUPAC) system would allow a utility such as an energy supplier and its consumers to better predict electricity grid activity and then, optimize its energy production, management, distribution, and consumption. The more accurate the prediction the more positive its economic and environmental impacts will be. A JUPAC system at a consumer collects ambient parameters, user patterns, and energy usage before by exploiting an embedded machine learning algorithm it predicts the consumer's future consumption This prediction may be recurrently transmitted to the energy supplier as a formatted commitment then, in a second time, the same device will try to respect this commitment by adjusting, wisely, the user appliances and heating—ventilation and air conditioning.

Abstract: A device for negotiating with a remote resource allocation server, an optimized resource allocation, comprises an input for entering a value of required resource and a time period for receiving the required resource and a processor for generating a utility function for the required resource and time period, generating a resource allocation request based on the utility function, sending the resource allocation request to the remote resource allocation server, receiving a capacity status notification for the required resource from the remote resource allocation server, generating an updated resource allocation request based on: the utility function, an Additive Increase Multiplicative Decrease (AIMD) algorithm and the required resource capacity status and iteratively repeating sending the updated resource allocation request and receiving the required resource capacity status from the remote resource allocation server and generating the updated resource allocation request until the updated resource allocation re

Abstract: Pricing a commodity based on a forecast received via remote access over a computer network. The forecast is an amount of the commodity that the user will use over a given time period. The provider supplies the user with an actual amount of the commodity over the given time period and then determines a price for the user for the supplied commodity, in retrospect, after the given time period, for the actual amount of the commodity supplied to the user over the given time period, based on the actual amount of the commodity provided to the user and an accuracy of the forecast of the amount of the commodity received from the user, the accuracy being determined by a difference between said forecast and the actual amount of the commodity supplied to the user over the given time period. The user is then charged to determined price.

Abstract: A method, system and computer program product for distributed forecasting and pricing for a commodity supplied by a provider to a user. In one embodiment, the provider announces a pricing plan, and the provider receives a forecast of an estimated amount of the commodity from the provider that the user will use over a given period of time. The provider provides the user with an actual amount of the commodity over the given period; and the provider charges the user for the actual amount of the commodity provided to the user based on the pricing policy, the actual amount of the commodity provided to the user, and the accuracy of the forecast. In an embodiment, the forecast is received by the provider after the provider announces the pricing plan and before the given period begins. The forecast may be received from the user or from a third-party forecaster.

Abstract: A method and system of providing hot water efficiently. A cost of heating a volume of water to a predetermined temperature in a first water heater is determined. A cost of transferring a volume of hot water from a second water heater of a network of interconnected water heaters to the first water heater is determined. Upon determining that the cost of transferring the volume of hot water from the second water heater is lower than the cost of heating the volume of water of the first water heater, the second water heater transfers at least part of the volume of hot water to the first water heater. Otherwise, the volume of water is heated to the predetermined temperature in the first water heater.

Abstract: A method and system of providing hot water efficiently. A cost of heating a volume of water to a predetermined temperature in a first water heater is determined. A cost of transferring a volume of hot water from a second water heater of a network of interconnected water heaters to the first water heater is determined. Upon determining that the cost of transferring the volume of hot water from the second water heater is lower than the cost of heating the volume of water of the first water heater, the second water heater transfers at least part of the volume of hot water to the first water heater. Otherwise, the volume of water is heated to the predetermined temperature in the first water heater.

Abstract: A yield management system, method, and non-transitory recording medium for an event, including modeling demand for tickets for the event to create a demand model, analyzing data comprising recent consumer purchases of the tickets for the event and recent advertising for the same event, and adjusting decision variables, comprising advertising spending for the event, the split thereof to various media, and the price of tickets, based on the data and the demand model.

Abstract: Embodiments of the disclosure include a system for providing data-driven distributionally robust optimization the system including a processor, the processor configured to perform a method. The method includes receiving a plurality of samples of one or more uncertain parameters for a complex system and calculating a distribution uncertainty set for the one or more uncertain parameters. The method also includes receiving a deterministic problem model associated with the complex system that includes an objective and one or more constraints and creating a distributionally robust counterpart (DRC) model based on the distribution uncertainty set and the deterministic problem model. The method further includes formulating the DRC as a generalized problem of moments (GPM), applying a semi-definite programming (SDP) relaxation to the GPM and generating an approximation for a globally optimal distributionally robust solution to the complex system.

Abstract: In one embodiment, a computer-implemented method includes receiving a plurality of belief signals from a plurality of parties, where each belief signal indicates a piece of information that a party providing the belief signal believes to be true. A request for an insight is received from an interested party. An insight is determined by evaluating, by a computer processor, a subset of the belief signals. Payment is received from the interested party for the insight. The parties who provided the subset of belief of signals are compensated out of the payment, based on their contribution to the determining the insight.

Abstract: In one embodiment, a computer-implemented method includes receiving a plurality of belief signals from a plurality of parties, where each belief signal indicates a piece of information that a party providing the belief signal believes to be true. A request for an insight is received from an interested party. An insight is determined by evaluating, by a computer processor, a subset of the belief signals. Payment is received from the interested party for the insight. The parties who provided the subset of belief of signals are compensated out of the payment, based on their contribution to the determining the insight.

Abstract: A method, system and computer program product for distributed forecasting and pricing for a commodity supplied by a provider to a user. In one embodiment, the provider announces a pricing plan, and the provider receives a forecast of an estimated amount of the commodity from the provider that the user will use over a given period of time. The provider provides the user with an actual amount of the commodity over the given period; and the provider charges the user for the actual amount of the commodity provided to the user based on the pricing policy, the actual amount of the commodity provided to the user, and the accuracy of the forecast. In an embodiment, the forecast is received by the provider after the provider announces the pricing plan and before the given period begins. The forecast may be received from the user or from a third-party forecaster.

Abstract: Embodiments of the disclosure include a method for providing data-driven distributionally robust optimization. The method includes receiving a plurality of samples of one or more uncertain parameters for a complex system and calculating a distribution uncertainty set for the one or more uncertain parameters. The method also includes receiving a deterministic problem model associated with the complex system that includes an objective and one or more constraints and creating a distributionally robust counterpart (DRC) model based on the distribution uncertainty set and the deterministic problem model. The method further includes formulating the DRC as a generalized problem of moments (GPM), applying a semi-definite programing (SDP) relaxation to the GPM and generating an approximation for a globally optimal distributionally robust solution to the complex system.

Abstract: Embodiments of the disclosure include a system for providing data-driven distributionally robust optimization the system including a processor, the processor configured to perform a method. The method includes receiving a plurality of samples of one or more uncertain parameters for a complex system and calculating a distribution uncertainty set for the one or more uncertain parameters. The method also includes receiving a deterministic problem model associated with the complex system that includes an objective and one or more constraints and creating a distributionally robust counterpart (DRC) model based on the distribution uncertainty set and the deterministic problem model. The method further includes formulating the DRC as a generalized problem of moments (GPM), applying a semi-definite programming (SDP) relaxation to the GPM and generating an approximation for a globally optimal distributionally robust solution to the complex system.

Abstract: A rehabilitation device includes a moving body, a motor, a control system and a sensor module. The moving body has a housing and a moving mechanism. The motor, disposed in the housing, connects to the moving mechanism and drives the moving mechanism. The control system is disposed in the housing and coupled to the motor. The sensor module is disposed on the moving body and coupled to the control module. The control system controls the operation of the motor to move the moving body according to information detected by the sensor module.

Abstract: A rehabilitation device includes a moving body, a motor, a control system and a sensor module. The moving body has a housing and a moving mechanism. The motor, disposed in the housing, connects to the moving mechanism and drives the moving mechanism. The control system is disposed in the housing and coupled to the motor. The sensor module is disposed on the moving body and coupled to the control module. The control system controls the operation of the motor to move the moving body according to information detected by the sensor module.