Systems and methods for forward market price prediction and sale of energy storage capacity

1. A transaction-enabling system, comprising:

a fleet of machines having an aggregate energy storage capacity; and

a controller, comprising:

an external data circuit structured to monitor an external data source and collect data from the external data source;

an expert system circuit structured to:

predict a forward market price for energy storage capacity based on the collected data and the aggregate energy storage capacity, including:

automatically generate a forecast for the forward market price for energy storage capacity in a forward market for energy storage capacity, the forecast being based at least in part on an automated agent behavior collected from at least one automated agent behavioral data source, the energy storage capacity being associated with at least one renewable energy system;

maintain a training set comprising feedback data indicating outcomes of previous forecasts and at least one of: facility parameters of the fleet of machines, yield of the fleet of machines, profitability of the fleet of machines, optimization of resources for the fleet of machines, optimization of business objectives for the fleet of machines, satisfaction of goals for the fleet of machines, satisfaction of users of the fleet of machines, or satisfaction of operators of the fleet of machines; and

train an artificial intelligence system based on the training data set, the training the artificial intelligence system including:

updating the training data set with the feedback data; and

iteratively self-adjusting the forecast for the forward market price of energy storage capacity based on the updated training data that includes the feedback data;

a smart contract circuit structured to automatically sell at least a subset of the aggregate energy storage capacity on the forward market for energy storage capacity in response to the predicted forward market price; and

an optimization neural network structured to:

determine respective allocations of energy storage capacity, among the fleet of machines, for energy storage for future computing tasks and sale of energy storage capacity on the forward market for energy storage capacity, based on the predicted forward market price, the optimization neural network being trained to iteratively self-adjust the respective allocations of energy storage capacity based on feedback data indicating facility outcomes for the fleet of machines and one or more of: the facility parameters of the fleet of machines and data collected from the fleet of machines, the facility outcomes for the fleet of machines comprising one or more of: outcomes based on yield, profitability, optimization of resources, optimization of business objectives, satisfaction of goals, and satisfaction of users or operators.

2. The system of claim 1 , wherein the controller further comprises a resource allocation circuit that allocates energy storage capacity among the fleet of machines.

3. The system of claim 1 , wherein the external data source comprises a social media source.

4. The system of claim 3 , wherein the social media source comprises one or more of: information publicly available on a social media site, information publicly available on a mass media platform, information from a public comments section of a news article; information from a review section of an online retailer; information from a publicly available profile; proprietary information properly obtained from a social media site; and proprietary information properly obtained from a mass media platform.

5. The system of claim 3 , wherein the social media source comprises cross referenced information from a further data source comprising one or more of: an IoT data source, an automated agent behavioral data source, a business entity data source, and a human behavioral data source.

6. The system of claim 1 , wherein the external data source comprises an automated agent behavioral data source.

7. The system of claim 1 , wherein the external data source comprises a business entity behavioral data source.

8. The system of claim 1 , wherein the external data source comprises a human entity behavioral data source.

9. The system of claim 1 , wherein the external data source comprises a spot market price for an energy storage capacity.

10. The system of claim 9 , wherein the smart contract circuit further comprises an arbitrage circuit structured to test a spot market for energy storage capacity.

11. The system of claim 1 , wherein the prediction of forward market price for energy storage capacity is at least partially based on a time value of the energy storage capacity, a geographical origin of the energy, a type of energy, or a non-linear consideration of a cost of an energy storage capacity.

12. The system of claim 1 , wherein the expert system circuit is further structured to predict a forward market pricing of energy credits based on the collected data.

13. The system of claim 12 , wherein the smart contract circuit is further structured to automatically sell or purchase an energy credit on a forward market for energy credits.

14. The system of claim 1 , wherein the artificial intelligence system comprises at least one of: a machine learning component, an artificial intelligence component, or a neural network component.

15. A method, comprising:

monitoring an external data source and collecting external data from the external data source;

predicting a forward market price for energy storage capacity, including:

automatically generating a forecast for the forward market price for energy storage capacity in a forward market for energy storage capacity, the forecast being based at least in part on an automated agent behavior collected from at least one automated agent behavioral data source, the energy storage capacity being associated with at least one renewable energy system;

maintaining a training set comprising feedback data indicating outcomes of previous forecasts and at least one of: facility parameters of the fleet of machines, yield of the fleet of machines, profitability of the fleet of machines, optimization of resources for the fleet of machines, optimization of business objectives for the fleet of machines, satisfaction of goals for the fleet of machines, satisfaction of users of the fleet of machines, or satisfaction of operators of the fleet of machines; and

train an artificial intelligence system based on the training data set, the training the artificial intelligence system including:

updating the training data set with the feedback data; and

iteratively self-adjusting the forecast for the forward market price of energy storage capacity based on the updated training data that includes the feedback data;

allocating an aggregate energy storage capacity among a fleet of machines;

automatically selling at least a subset of the aggregated energy storage capacity on the forward market in response to the predicted forward market price; and

determining, by an optimization neural network, respective allocations of energy storage capacity, among the fleet of machines, for energy storage for future computing tasks and sale of energy storage capacity on the forward market for energy storage capacity, based on the predicted forward market price, the optimization neural network being trained to iteratively self-adjust the respective allocations of energy storage capacity based on feedback data indicating facility outcomes for the fleet of machines and one or more of: the facility parameters of the fleet of machines and data collected from the fleet of machines, the facility outcomes for the fleet of machines comprising one or more of: outcomes based on yield, profitability, optimization of resources, optimization of business objectives, satisfaction of goals, and satisfaction of users or operators.

16. The method of claim 15 , wherein the external data source comprises one or more of: a social media source, an automated behavioral source, a business entity behavioral source, a human entity behavioral source, and a spot market price for an energy storage capacity.

17. The method of claim 15 , wherein the prediction of forward market price for energy storage capacity is at least partially based on a time value of the energy storage capacity, a geographical origin of the energy, a type of energy, or a non-linear consideration of a cost of the energy storage capacity.

18. The method of claim 15 , wherein the artificial intelligence system comprises at least one of: a machine learning component, an artificial intelligence component, or a neural network component.