Patent ID: 11966840
Assignee: NOODLE ANALYTICS, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G  B | IPC G

Claim 10:
11. A method, comprising:
building a deep probabilistic decision machine (DPDM) using historical and current data from a real process and using knowledge of causal interactions, a state of the real process comprising actions, conditions, sensors, and targets of the process;
representing, by a deep probabilistic (DP) simulated process of the DPDM, a current state of the real process based on a past sequence of actions, conditions, sensors, and targets;
predicting, by the DP simulated process of the DPDM, a next state for simulated actions and conditions at the current state of the real process;
predicting, by the DP simulated process of the DPDM, a next target and sensor observations based on the predicted next state of the real process; and
generating and selecting, by a decision-making (DM) controller of the DPDM, simulated actions for conditions at the current state to maximize a total cumulative utility over a future predicted sequence;
wherein the real process is for controlling energy consumption in a manufacturing process with rechargeable batteries; the actions including setting battery discharging during peak usage to reduce direct energy usage from an electricity supplier and battery recharging during periods with electricity cost below an average electricity cost; the conditions including one or more of raw material, steel grade, and temperature; and
the targets including consumed energy per unit of time and a predefined peak-usage per period.