Generating set of curtailment actions predicted to achieve net consumption target for a subperiod

1. A method, comprising:

providing, by processing circuitry, a net consumption trajectory comprising net consumption targets for one or more subperiods of a time period, wherein each net consumption target indicates a target difference from a beginning of a time period to an end of the subperiod between total consumption and total production or offset;

generating, by the processing circuitry and for a subperiod of the plurality of subperiods, a set of curtailment actions predicted to achieve the net consumption target for the subperiod; and

implementing, by the processing circuitry, the set of curtailment actions.

2. The method of claim 1 , wherein the net consumption target is a net energy target indicating a target difference between total energy consumption and total energy production, wherein the subperiod starts after the beginning of the time period.

3. The method of claim 2 , wherein the total energy consumption corresponds to energy used by a facility and the total energy production corresponds to green energy produced at the facility.

4. The method of claim 1 , wherein the net consumption target is a net carbon target indicating a target difference between total carbon emissions and total carbon capture from a beginning of the time period to an end of the subperiod.

5. The method of claim 1 , further comprising:

providing, by the processing circuitry, a user dashboard comprising a visualization of the net consumption trajectory and the curtailment actions; and

updating, by the processing circuitry, the user dashboard as the plurality of subperiods elapse.

6. The method of claim 1 , wherein providing the net consumption trajectory comprises generating the net consumption trajectory as an output of a predictive optimization constrained to cause the net consumption trajectory to have a target value at an end of the time period.

7. The method of claim 1 , wherein implementing the curtailment actions comprises deploying a device of building equipment specified by the curtailment actions.

8. The method of claim 1 , wherein implementing the curtailment actions comprises controlling equipment to execute the curtailment actions.

9. The method of claim 8 , wherein the equipment comprises a lighting device and a HVAC device, and wherein the set of curtailment actions comprises a lighting level change and a temperature setpoint change.

10. The method of claim 1 , wherein providing the net consumption trajectory comprises performing a predictive optimization at a beginning of the time period; and

wherein generating the set of curtailment actions is performed at a beginning of the subperiod.

11. The method of claim 1 , wherein the total consumption comprises a first portion associated with building equipment and a second portion associated with transportation vehicles.

12. The method of claim 1 , further comprising predicting, by the processing circuitry, an amount of energy production for the time period using a neural network, wherein the net consumption trajectory is based on the amount of energy production for the time period.

13. The method of claim 1 , wherein generating the set of curtailment actions comprises using a digital twin of a facility and wherein implementing the set of curtailment actions comprises operating equipment represented in the digital twin.

14. The method of claim 1 , wherein generating the set of curtailment actions comprises allocating portions of a total curtailment amount across a plurality of types of equipment.

15. A system comprising:

an energy load operable to consume energy;

a green energy source configured to produce energy; and

processing circuitry programmed to:

provide a net energy trajectory comprising net energy targets for one or more subperiods of a time period, the net energy targets indicating gaps between energy to be consumed by the energy load and energy to be produced by the green energy source;

generate, for a subperiod of the plurality of subperiods, a curtailment action predicted to achieve the net energy target for the subperiod; and

implement the curtailment action by affecting the energy load.

16. The system of claim 15 , wherein the net energy target for the subperiod indicates a target difference between cumulative energy consumption by the energy load and cumulative energy production by the green energy source from a beginning of the time period to an end of the subperiod, wherein the subperiod starts after the beginning of the time period.

17. The system of claim 15 , wherein the system is configured to generate the curtailment action by performing a predictive optimization that uses cost predictions for possible curtailment actions and energy shaving predictions for the possible curtailment actions.

18. The system of claim 15 , wherein:

the processing circuitry is programmed to perform a first predictive optimization and a second predictive optimization;

the net energy trajectory is an output of the first predictive optimization an input to the second predictive optimization; and

the curtailment action is an output of the second predictive optimization.

19. The system of claim 18 , wherein the first predictive optimization is constrained to cause the net energy trajectory to have a zero value at an end of the time period.

20. The system of claim 18 , wherein the first predictive optimization is performed at a beginning of the time period and the second predictive optimization is performed at a beginning of the subperiod, the beginning of the subperiod after the beginning of the time period.

21. The system of claim 15 , wherein the processing circuitry is programmed to generate the set of curtailment actions comprises using a digital twin of a facility and wherein implementing the set of curtailment actions comprises operating equipment represented in the digital twin.