Predicting Measurements at Unobserved Locations in an Electrical Transmission System
| dc.contributor.author | Surmann, Dirk | |
| dc.contributor.author | Ligges, Uwe | |
| dc.contributor.author | Weihs, Claus | |
| dc.date.accessioned | 2016-01-08T08:12:58Z | |
| dc.date.available | 2016-01-08T08:12:58Z | |
| dc.date.issued | 2016-01-06 | |
| dc.description.abstract | Electrical Transmission Systems consist of a huge number of locations (nodes) with different types of measurements available. Our aim is to derive a subset of nodes which contains almost sufficient information to describe the whole energy network. We derive a parameter set which characterises every single measuring location or node, respectively. Via analysing the behaviour of each node with respect to its neighbours, we construct a feasible random field metamodel over the whole transmission system. The metamodel works in a discrete spatial domain to smooth the measurements across the network. In the next step we work with a subset of locations to predict the unobserved ones. We derive different graph kernels to define the missing covariance matrix from the neighbourhood structures of the network. This results in a metamodel that is able to predict unobserved locations in a spatial domain with non-isotropic distance functions. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/34441 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-16497 | |
| dc.language.iso | en | de |
| dc.subject | Measurement Prediction | en |
| dc.subject | Discrete Random Field Model | en |
| dc.subject | Graph Kernels | en |
| dc.subject.ddc | 004 | |
| dc.subject.ddc | 310 | |
| dc.subject.ddc | 620 | |
| dc.title | Predicting Measurements at Unobserved Locations in an Electrical Transmission System | en |
| dc.type | Text | de |
| dc.type.publicationtype | preprint | de |
| dcterms.accessRights | open access |