Systems and methods for aggregating transactions and optimization data related to energy and energy credits

1. A transaction-enabling system, comprising:

a fleet of instrumented machines, each of the instrumented machines having an energy requirement for a task and at least one instrument that provides energy usage data about the instrumented machine; and

a controller, comprising one or more high-speed processing devices, that includes:

a resource requirement system, executed by at least one of the one or more high-speed processing devices, that aggregates a resource requirement for the fleet of instrumented machines to perform a task by observing at least one set of operations performed by the fleet of instrumented machines while receiving energy usage data from at least one of the instrumented machines, wherein the aggregated resource requirement comprises an energy credit requirement;

a forward resource market system, executed by at least one of the one or more high-speed processing devices, in communication with and capable of executing a transaction on a forward market for energy; and

a resource distribution system, executed by at least one of the one or more high-speed processing devices and including an expert system, the expert system comprising at least one of a machine learning component, an artificial intelligence component, or a neural network;

wherein the resource distribution system, responsive to the aggregated resource requirement provided by the resource requirement system, automatically executes a transaction on the forward market for energy via the forward resource market system; and

wherein the expert system is continuously trained to improve a utilization of energy or energy credits associated with the fleet of instrumented machines using a training data set based on a plurality of transactions on the forward market for energy by adaptively improving one of an aggregate output value of the fleet of instrumented machines or a cost of operation of the fleet of instrumented machines.

2. The system of claim 1 , wherein the aggregated resource requirement is one of a compute task requirement, a networking task requirement, or an energy consumption task requirement.

3. The system of claim 1 , wherein the transaction executed by the resource distribution system on the forward market for energy comprises one of buying or selling energy.

4. The system of claim 1 , wherein the transaction executed by the resource distribution system on the forward market for energy comprises one of buying or selling energy credits.

5. The system of claim 1 , further comprising a market forecasting system that predicts a forward market price of energy on the forward market for energy.

6. The system of claim 1 , wherein the resource distribution system further interprets historical external data from at least one external data source, and further adaptively improves a utilization of one of energy or energy credits in response to the historical external data.

7. The system of claim 6 , wherein the at least one external data source is one of a market condition data source, a behavioral data source, an agent data source, and an historical outcome data source.

8. The system of claim 1 , further comprising a forecasting circuit system that structured to adaptively improve improves a forecast for an energy resource price on the forward market for energy using at least one of a machine learning component, an artificial intelligence component, or a neural network component.

9. The system of claim 1 , further comprising a market forecasting system that predicts a forward market price of energy credits on the forward market for energy.

10. The system of claim 9 , wherein the resource distribution system further interprets historical external data from at least one external data source, and further adaptively improves a utilization of energy credits in response to the historical external data.

11. The system of claim 10 , wherein the at least one external data source is one of a market condition data source, a behavioral data source, an agent data source, or an historical outcome data source.

12. A method, comprising:

monitoring, by a resource requirement system, at least one operation performed by a fleet of instrumented machines, each of the instrumented machines having an energy requirement for a task and at least one instrument that provides energy usage data for the instrumented machine, while receiving energy usage data from at least one of the instrumented machines;

determining, by the resource requirement system monitoring, an aggregated resource amount required for the fleet of instrumented machines to service at least one task, wherein the aggregated resource amount comprises an energy credit requirement;

accessing a forward market for energy by a forward resource market system;

automatically executing a transaction on the forward market for energy using the forward resource market system in response to the aggregated resource amount determined by the resource requirement system; and

training a resource distribution system, comprising an expert system that includes at least one of a machine learning component, an artificial intelligence component, or a neural network, to improve a utilization of energy or energy credits associated with the fleet of instrumented machines using a training data set based on a plurality of transactions on the forward market for energy by adaptively improving one of an aggregate output value of the fleet of instrumented machines or a cost of operation of the fleet of instrumented machines.

13. The method of claim 12 , wherein the at least one task comprises at least one of a compute task, a networking task, and an energy consumption task.

14. The method of claim 12 , wherein executing the transaction on the forward market for energy comprises one of buying or selling energy credits.

15. The method of claim 12 , wherein executing the transaction on the forward market for energy comprises one of buying or selling energy.

16. The method of claim 12 , further comprising forecasting to adaptively improve a forecast for an energy resource price on the forward market for energy using a machine learning component, an artificial intelligence component, or a neural network component.

17. The method of claim 12 , further comprising interpreting historical external data from at least one external data source, and further adaptively improving a utilization of the aggregated resource amount in response to the historical external data.

18. The method of claim 17 , wherein the at least one external data source is one of a market condition data source, a behavioral data source, an agent data source, or an historical outcome data source.