US 12,463,431 B2
Unsupervised solar energy disaggregation system and method
Hakan Ozlemis, Princeton, NJ (US); Ulrich Muenz, Princeton, NJ (US); Siddharth Bhela, Kendall Park, NJ (US); Xiaofan Wu, North Brunswick, NJ (US); Michael Bernhard Buhl, Grafing (DE); Daniel Kloeser, Eckernfoerde (DE); and Coral Siminovich, Oakville (CA)
Assigned to Siemens Aktiengesellschaft, Munich (DE)
Filed by Siemens Aktiengesellschaft, Munich (DE)
Filed on Dec. 20, 2022, as Appl. No. 18/068,678.
Prior Publication US 2024/0204530 A1, Jun. 20, 2024
Int. Cl. H02J 3/38 (2006.01); H02J 3/00 (2006.01)
CPC H02J 3/381 (2013.01) [H02J 3/003 (2020.01); H02J 3/004 (2020.01); H02J 2203/10 (2020.01); H02J 2203/20 (2020.01); H02J 2300/26 (2020.01)] 12 Claims
OG exemplary drawing
 
1. A computer-implemented method for estimating or predicting an aggregate solar generation at a node of a distribution system, the method comprising:
obtaining, by a computing system comprising one or more processors, first data pertaining to a net power measured by a meter installed at the node, the first data comprising a number of samples of net power averaged over a defined interval,
obtaining, by the computing system, second data pertaining to a solar irradiation local to the node, the second data comprising a number of samples of solar irradiation averaged over intervals temporally correlated with the first data,
training, by the computing system, a physics-based model of the node using the first and second data, to optimize a set of model parameters, the physics-based model being defined such that an average net power includes a composite of an average solar generation and an average power consumption at the node and the average solar generation is modeled as a function of an average solar irradiation, and
estimating or predicting, by the computing system, an aggregate solar generation at the node from an input respectively comprising actual irradiation data or irradiation forecast data local to the node, using a model parameter optimized by the training.