Systems and methods for fleet forward energy and energy credits purchase

1. A transaction-enabling system, comprising:

a fleet of machines each having an energy consumption task requirement; and

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

a resource requirement circuit, executed by at least one of the one or more high-speed processing devices, that aggregates a resource requirement for the fleet of machines to perform a task and obtains energy usage data for the fleet of machines;

a forward resource market circuit, executed by at least one of the one or more high-speed processing devices, that accesses a forward market for energy and obtains energy market data; and

a resource distribution circuit, executed by at least one of the one or more high-speed processing devices and comprising an expert system, the expert system comprising one of a machine learning component, an artificial intelligence component, or a neural network and that executes a transaction on the forward market for energy via the forward resource market circuit in response to the aggregated resource requirement provided by the resource requirement circuit;

wherein the expert system within the resource distribution circuit, responsive to the aggregated resource requirement provided by the resource requirement circuit and the energy market data provided by the forward resource market circuit, automatically executes at least one of:

allocating delivery of a first amount of energy to the fleet of machines; or

initiating a transaction for a second amount of energy on the forward market for energy; and

wherein the resource distribution circuit is continuously trained to improve the allocation of energy delivery to the fleet of machines using the energy usage data provided by the resource requirement circuit and 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 machines or a cost of operation of the fleet of machines.

2. The system of claim 1 , wherein the resource requirement is a requirement selected from a group consisting of: a compute task requirement, a networking task requirement, and an energy consumption task requirement.

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

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

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

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

7. The system of claim 6 , wherein the at least one external data source is at least 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 that adaptively improves 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.

9. The system of claim 1 , further comprising a market forecasting circuit 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 circuit 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 at least one of a market condition data source, a behavioral data source, an agent data source, and an historical outcome data source.

12. A method, comprising:

determining, via a resource requirement circuit, an aggregated resource amount required for a fleet of machines to service at least one task;

obtaining, via the resource requirement circuit, energy usage data for the fleet of machines;

accessing a forward market for energy via a forward resource market circuit and obtaining energy market data;

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

automatically executing, via a resource distribution circuit comprising an expert system that includes one of a machine learning component, an artificial intelligence component, or a neural network and responsive to the aggregated resource requirement provided by the resource requirement circuit and the energy market data provided by the forward market circuit, at least one of:

allocating a delivery of a first amount of energy to the fleet of machines; or

initiating a transaction for a second amount of energy on the forward market for energy; and

training the expert system within the resource distribution circuit to improve the allocation of energy to the fleet of machines using the energy usage data provided by the resource requirement circuit and a training data set based on a plurality of transactions on the forward market for energy by adaptively improving one of an aggregated output value of the fleet of machines or a cost of operation of the fleet of 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 at least one of a market condition data source, a behavioral data source, an agent data source, and an historical outcome data source.