Probabilistic solar power forecasting in smart grids using distributed information

dc.contributor.author Ricardo Jorge Bessa en
dc.contributor.author Trindade,A en
dc.contributor.author Silva,CSP en
dc.contributor.author Vladimiro Miranda en
dc.date.accessioned 2017-11-23T11:57:55Z
dc.date.available 2017-11-23T11:57:55Z
dc.date.issued 2015 en
dc.description.abstract The deployment of Smart Grid technologies opens new opportunities to develop new forecasting and optimization techniques. The growth of solar power penetration in distribution grids imposes the use of solar power forecasts as inputs in advanced grid management functions. This paper proposes a new forecasting algorithm for 6 h ahead based on the vector autoregression framework, which combines distributed time series information collected by the Smart Grid infrastructure. Probabilistic forecasts are generated for the residential solar photovoltaic (PV) and secondary substation levels. The test case consists of 44 micro-generation units and 10 secondary substations from the Smart Grid pilot in Evora, Portugal. The benchmark model is the well-known autoregressive forecasting method (univariate approach). The average improvement in terms of root mean square error (point forecast evaluation) and continuous ranking probability score (probabilistic forecast evaluation) for the first 3 lead-times was between 8% and 12%, and between 1.4% and 5.9%, respectively. (C) 2015 Published by Elsevier Ltd. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/3810
dc.identifier.uri http://dx.doi.org/10.1016/j.ijepes.2015.02.006 en
dc.language eng en
dc.relation 4882 en
dc.relation 208 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Probabilistic solar power forecasting in smart grids using distributed information en
dc.type article en
dc.type Publication en
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
P-00G-AR4.pdf
Size:
1.04 MB
Format:
Adobe Portable Document Format
Description: