Forward market renewable energy credit prediction from human behavioral data

1. A transaction-enabling system, comprising:

a forward market circuit structured to access a forward energy credit market;

a market forecasting circuit structured to:

automatically generate a forecast for a forward market price of an energy credit in the forward energy credit market, the forecast being based at least in part on human behavior information collected from at least one human behavioral data source, the energy credit comprising a renewable energy credit associated with at least one renewable energy system;

maintain a training data set comprising feedback data indicating outcomes of previous forecasts 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

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 the energy credit based on the updated training data that includes the feedback data of the training data set; and

a smart contract circuit structured to perform at least one of selling the renewable energy credit or purchasing the renewable energy credit on the forward energy credit market in response to the forecasted forward market price of the energy credit.

2. The system of claim 1 , wherein the artificial intelligence of the market forecasting circuit further comprises at least one of a machine learning component, an artificial intelligence component, or a neural network component.

3. The system of claim 1 , wherein the human behavior information includes information collected from automated agent behavioral data sources.

4. The system of claim 1 , wherein the human behavior information includes information obtained by analyzing at least one of social media posts or e-commerce data.

5. The system of claim 1 , wherein the human behavior information includes at least one of: online behavior, mobility behavior, energy consumption behavior, energy production behavior, network utilization behavior, compute and processing behavior, resource consumption behavior, resource production behavior, purchasing behavior, attention behavior, or social behavior.

6. The system of claim 1 , wherein the at least one renewable energy system includes at least one of: a wind farm, a solar source, a hydroelectric source, a biomass source, hydrogen fuel cells, or a geothermal source.

7. The system of claim 1 , wherein the at least one renewable energy system comprises an energy storage capacity.

8. The system of claim 1 , wherein the market forecasting circuit is further structured to adaptively improve the forecast for the forward market price using the human behavior information.

9. A method, comprising:

accessing a forward energy credit market;

collecting human behavior information from at least one human behavioral data source;

generating a forecast for a forward market price of energy credits in the forward energy credit market the forecast being based at least in part on the human behavior information;

maintaining a training data set comprising feedback data indicating outcomes of previous forecasts 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;

training 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 an energy credit based on the updated training data that includes the feedback data of the training data set; and

performing at least one of selling a renewable energy credit for at least one renewable energy system or purchasing a renewable energy credit for the at least one renewable energy system, on the forward energy credit market in response to the forecasted forward market price of energy credits.

10. The method of claim 9 , further comprising collecting the human behavior information from automated agent behavioral data sources.

11. The method of claim 9 , wherein collecting the human behavior information further includes analyzing data from at least one of social media posts or e-commerce data.

12. The method of claim 9 , wherein the human behavior information includes at least one of: online behavior, mobility behavior, energy consumption behavior, energy production behavior, network utilization behavior, compute and processing behavior, resource consumption behavior, resource production behavior, purchasing behavior, attention behavior, or social behavior.

13. The method of claim 9 , wherein the at least one renewable energy system includes at least one of: a wind farm, a solar source, a hydroelectric source, a biomass source, hydrogen fuel cells, or a geothermal source.

14. The method of claim 9 , wherein the at least one renewable energy system comprises an energy storage capacity.

15. The method of claim 9 , further comprising adaptively improving the forecast for the forward market price using the human behavior information.

16. A transaction-enabling system, comprising:

a set of processors; and

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

access a forward energy credit market;

automatically generate a forecast for a forward market price of energy credits in the forward energy credit market, the forecast being based at least in part on a human behavior information collected from at least one human behavioral data source;

sell an energy credit in the forward energy credit market, the energy credit being associated with at least one renewable energy system;

maintain a training data set comprising feedback data indicating outcomes of previous forecasts 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;

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 the energy credit based on the updated training data that includes the feedback data of the training data set; and

perform at least one of selling the energy credit or purchasing the energy credit on the forward energy credit market in response to the forecasted forward market price of the energy credit.

17. The system of claim 16 , wherein the training the artificial intelligence further comprises training at least one of a machine learning component, an artificial intelligence component, or a neural network component.

18. The system of claim 16 , wherein the human behavior information includes information obtained by analyzing from at least one of social media posts or e-commerce data.

19. The system of claim 16 , wherein the human behavior information includes at least one of: online behavior, mobility behavior, energy consumption behavior, energy production behavior, network utilization behavior, compute and processing behavior, resource consumption behavior, resource production behavior, purchasing behavior, attention behavior, or social behavior.

20. The system of claim 16 , wherein the at least one renewable energy system includes at least one of: a wind farm, a solar source, a hydroelectric source, a biomass source, hydrogen fuel cells, or a geothermal source.