Very short term load prediction
View Patent ↗Certain exemplary embodiments provide a method comprising: providing system electrical load information for an electric power system to one or more neural networks; and via the one or more neural networks, predicting a system electrical load for a predetermined immediate future period.
1. A method, comprising:
receiving system electrical load information for an electric power system at one or more neural networks;
via the one or more neural networks, predicting a system electrical load for a predetermined immediate future period in predetermined intervals; and
providing the predicted system electrical load to a Dynamic Economic Dispatch module adapted to use a Lagrange Multiplier approach to determine a response to the predicted system electrical load.
2. The method of claim 1 , further comprising:
receiving an identification of a source of the system electrical load information.
3. The method of claim 1 , further comprising:
obtaining system electrical load information.
4. The method of claim 1 , further comprising:
obtaining historical system electrical load information.
5. The method of claim 1 , further comprising:
obtaining actual system electrical load information for a predetermined immediate past period.
6. The method of claim 1 , further comprising:
obtaining actual system electrical load information for a predetermined immediate past period in predetermined intervals.
7. The method of claim 1 , further comprising:
filtering the system electrical load information.
8. The method of claim 1 , further comprising:
normalizing the system electrical load information.
9. The method of claim 1 , further comprising:
smoothing the system electrical load information.
10. The method of claim 1 , further comprising:
determining patterns in the system electrical load information.
11. The method of claim 1 , further comprising:
classifying the system electrical load information.
12. The method of claim 1 , further comprising:
calculating parameters of the system electrical load information.
13. The method of claim 1 , further comprising:
training the one or more neural networks.
14. The method of claim 1 , wherein the one or more neural networks utilize a nonlinear dynamic model.
15. The method of claim 1 , wherein each of the one or more neural networks is responsible for a non-overlapping time period of a day.
16. The method of claim 1 , wherein the system electrical load information is provided to the one or more neural networks automatically at a predetermined frequency.
17. The method of claim 1 , wherein the system electrical load information is provided to the one or more neural networks manually.
18. The method of claim 1 , wherein the system electrical load information comprises incremental system load information.
19. The method of claim 1 , wherein the system electrical load information comprises absolute system load information.
20. A machine-readable medium containing instructions for activities comprising:
receiving system electrical load information for an electric power system at one or more neural networks;
via the one or more neural networks, predicting a system electrical load for a predetermined immediate future period in predetermined intervals; and
providing the predicted system electrical load to a Dynamic Economic Dispatch module adapted to use a Lagrange Multiplier approach to determine a response to the predicted system electrical load.
21. A system comprising:
one or more neural networks adapted to predict a system electrical load for an electric power system, the system electrical load predicted for a predetermined immediate future period in predetermined intervals;
a Dynamic Economic Dispatch module adapted to use a Lagrange Multiplier approach to determine a response to the predicted system electrical load, the predicted system electrical load received from said one or more neural networks; and
a user interface for specifying operations of said one or more neural networks.