US 11,816,604 B2
Systems and methods for forward market price prediction and sale of energy storage capacity
Charles Howard Cella, Pembroke, MA (US)
Assigned to Strong Force TX Portfolio 2018, LLC, Fort Lauderdale, FL (US)
Filed by Strong Force TX Portfolio 2018, LLC, Fort Lauderdale, FL (US)
Filed on Nov. 22, 2019, as Appl. No. 16/692,703.
Application 16/692,703 is a continuation of application No. PCT/US2019/030934, filed on May 6, 2019.
Claims priority of provisional application 62/787,206, filed on Dec. 31, 2018.
Claims priority of provisional application 62/751,713, filed on Oct. 29, 2018.
Claims priority of provisional application 62/667,550, filed on May 6, 2018.
Prior Publication US 2020/0097988 A1, Mar. 26, 2020
Int. Cl. G06Q 30/02 (2023.01); G06Q 10/06 (2023.01); H02J 3/00 (2006.01); H02J 3/28 (2006.01); H02J 3/14 (2006.01); G06Q 10/04 (2023.01); G06Q 30/0201 (2023.01); G06N 20/00 (2019.01); G06F 16/23 (2019.01); G06Q 30/0202 (2023.01); G06F 9/46 (2006.01); G06F 9/50 (2006.01); G06N 3/08 (2023.01); G06Q 10/0631 (2023.01); G06Q 10/067 (2023.01); G06Q 40/04 (2012.01); G06Q 50/06 (2012.01); G06Q 50/18 (2012.01); G06N 5/04 (2023.01); G06Q 20/22 (2012.01); G06Q 20/38 (2012.01); G06Q 30/0241 (2023.01); G06Q 30/0273 (2023.01); G06F 21/10 (2013.01); G06Q 20/40 (2012.01); G06F 9/48 (2006.01); G06Q 20/36 (2012.01); H04L 47/78 (2022.01); G06N 3/02 (2006.01); G06Q 20/06 (2012.01); G06F 16/951 (2019.01); G06F 9/38 (2018.01); H04L 47/783 (2022.01); G06Q 30/0204 (2023.01); G06Q 40/10 (2023.01); G06F 16/24 (2019.01); G06Q 50/04 (2012.01); H04L 9/00 (2022.01); H04L 12/14 (2006.01); H04L 47/70 (2022.01); G06F 16/18 (2019.01); H02J 3/38 (2006.01); G05B 19/00 (2006.01); G05B 19/418 (2006.01); G06F 9/54 (2006.01); G06Q 30/06 (2023.01); G06F 18/214 (2023.01); G06F 16/27 (2019.01); G06F 16/182 (2019.01); G06F 30/27 (2020.01); G06N 3/04 (2023.01); G06Q 50/00 (2012.01); H04L 9/06 (2006.01); G06F 16/2457 (2019.01); G06Q 30/0251 (2023.01); G06N 3/044 (2023.01); G06N 3/047 (2023.01); H04L 67/12 (2022.01)
CPC G06Q 10/04 (2013.01) [G05B 19/00 (2013.01); G05B 19/4188 (2013.01); G05B 19/41865 (2013.01); G06F 9/3836 (2013.01); G06F 9/3891 (2013.01); G06F 9/466 (2013.01); G06F 9/4806 (2013.01); G06F 9/4881 (2013.01); G06F 9/50 (2013.01); G06F 9/5005 (2013.01); G06F 9/5016 (2013.01); G06F 9/5027 (2013.01); G06F 9/5072 (2013.01); G06F 9/541 (2013.01); G06F 16/182 (2019.01); G06F 16/1865 (2019.01); G06F 16/23 (2019.01); G06F 16/2365 (2019.01); G06F 16/2379 (2019.01); G06F 16/24 (2019.01); G06F 16/27 (2019.01); G06F 16/951 (2019.01); G06F 18/2148 (2023.01); G06F 18/2155 (2023.01); G06F 21/105 (2013.01); G06F 30/27 (2020.01); G06N 3/02 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/067 (2013.01); G06Q 10/0631 (2013.01); G06Q 10/06314 (2013.01); G06Q 10/06315 (2013.01); G06Q 20/06 (2013.01); G06Q 20/065 (2013.01); G06Q 20/0655 (2013.01); G06Q 20/29 (2013.01); G06Q 20/367 (2013.01); G06Q 20/389 (2013.01); G06Q 20/38215 (2013.01); G06Q 20/405 (2013.01); G06Q 20/4016 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/0202 (2013.01); G06Q 30/0205 (2013.01); G06Q 30/0206 (2013.01); G06Q 30/0247 (2013.01); G06Q 30/0273 (2013.01); G06Q 30/06 (2013.01); G06Q 40/04 (2013.01); G06Q 40/10 (2013.01); G06Q 50/04 (2013.01); G06Q 50/06 (2013.01); G06Q 50/184 (2013.01); H02J 3/008 (2013.01); H02J 3/14 (2013.01); H02J 3/28 (2013.01); H02J 3/388 (2020.01); H04L 9/50 (2022.05); H04L 12/14 (2013.01); H04L 47/783 (2013.01); H04L 47/788 (2013.01); H04L 47/823 (2013.01); G05B 2219/36542 (2013.01); G06F 9/3838 (2013.01); G06F 16/2457 (2019.01); G06N 3/044 (2023.01); G06N 3/047 (2023.01); G06N 3/0418 (2013.01); G06Q 20/4015 (2020.05); G06Q 30/0254 (2013.01); G06Q 30/0276 (2013.01); G06Q 50/01 (2013.01); G06Q 2220/00 (2013.01); G06Q 2220/12 (2013.01); G06Q 2220/18 (2013.01); H02J 3/003 (2020.01); H04L 9/0643 (2013.01); H04L 67/12 (2013.01)] 18 Claims
OG exemplary drawing
 
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.