Systems and methods for improving resource utilization for a fleet of machines

1. A transaction-enabling system, comprising:

a regenerative energy facility;

a fleet of machines, each having a requirement for at least one of a compute task, a networking task, or an energy consumption task;

a controller; and

a non-transitory computer-readable medium storing a set of instructions that, when executed, cause the controller to:

determine an amount of a resource for each of the machines to service the requirement for the at least one of the compute task, the networking task, and the energy consumption task for each corresponding machine, the resource comprising an energy resource produced by the regenerative energy facility; and

adaptively improve a resource utilization of the resource for each requirement for each corresponding machine, the adaptively improving the resource utilization comprising:

maintaining a training data set for at least one of a machine learning component, an artificial intelligence component, or a neural network component, the training data set comprising feedback data indicating outcomes of previous resource utilization of the resource, historical prices for the resource on a market for the resource, and at least one of facility parameters, yield, profitability, optimization of resources, optimization of business objectives, satisfaction of goals, satisfaction of users, or satisfaction of operators; and

by the at least one of a machine learning component, an artificial intelligence component, or a neural network component, iteratively self-adjusting:

the resource utilization of the resource based on the feedback data of the training data set;

delivery, to the fleet of machines, of the resource produced by the regenerative energy facility; and

sale of excess energy, not delivered to the fleet of machines, produced by the regenerative energy facility on the market for the resource.

2. The system of claim 1 , wherein:

the resource further comprises a compute resource; and

the at least one of a machine learning component, an artificial intelligence component, or a neural network component, is further configured to iteratively self-adjust a sale of the compute resource on the market for the resource.

3. The system of claim 1 , wherein:

the resource further comprises a spectrum allocation resource; and

the at least one of a machine learning component, an artificial intelligence component, or a neural network component, is further configured to iteratively self-adjust a sale of the spectrum allocation resource on the market for the resource.

4. The system of claim 1 , wherein the resource further comprises an energy credit resource.

5. The system of claim 1 , wherein:

the resource further comprises a data storage resource; and

the at least one of a machine learning component, an artificial intelligence component, or a neural network component, is further configured to iteratively self-adjust a sale of the data storage resource on the market for the resource.

6. The system of claim 1 , wherein the resource further comprises an energy storage resource.

7. The system of claim 1 , wherein:

the resource further comprises a network bandwidth resource; and

the at least one of a machine learning component, an artificial intelligence component, or a neural network component, is further configured to iteratively self-adjust a sale of the network bandwidth resource on the market for the resource.

8. A method, comprising:

determining an amount of a resource, for each of machine of a fleet of machines, to service a requirement of at least one of a compute task, a networking task, or an energy consumption task for each corresponding machine, the resource comprising an energy resource produced by a regenerative energy facility associated with the fleet of machines; and

adaptively improving a resource utilization of the resource for each requirement for each corresponding machine, the adaptively improving the resource utilization comprising:

maintaining a training data set for at least one of a machine learning component, an artificial intelligence component, or a neural network component, the training data set comprising feedback data indicating outcomes of previous resource utilization of the resource, historical prices for the resource on a market for the resource, and at least one of facility parameters, yield, profitability, optimization of resources, optimization of business objectives, satisfaction of goals, satisfaction of users, or satisfaction of operators; and

by the at least one of a machine learning component, an artificial intelligence component, or a neural network component, iteratively self-adjusting:

the resource utilization of the resource based on the feedback data of the training data set; and

delivery, to the fleet of machines, of the resource produced by the regenerative energy facility; and

sale of excess energy, not delivered to the fleet of machines, produced by the regenerative energy facility on the market for the resource.

9. The method of claim 8 , wherein:

the resource further comprises a compute resource; and

the at least one of a machine learning component, an artificial intelligence component, or a neural network component, is further configured to iteratively self-adjust a sale of the compute resource on the market for the resource.

10. The method of claim 8 , wherein the resource further comprises a spectrum allocation resource; and

the at least one of a machine learning component, an artificial intelligence component, or a neural network component, is further configured to iteratively self-adjust a sale of the compute resource on the market for the resource.

11. The method of claim 8 , wherein the resource further comprises an energy credit resource.

12. The method of claim 8 , wherein:

the resource further comprises a data storage resource; and

the at least one of a machine learning component, an artificial intelligence component, or a neural network component, is further configured to iteratively self-adjust a sale of the data storage resource on the market for the resource.

13. The method of claim 8 , wherein the resource further comprises an energy storage resource.

14. The method of claim 8 , wherein:

the resource further comprises a network bandwidth resource; and

the at least one of a machine learning component, an artificial intelligence component, or a neural network component, is further configured to iteratively self-adjust a sale of the network bandwidth resource on the market for the resource.