Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/3540
Title: Solar power forecasting in smart grids using distributed information
Authors: Ricardo Jorge Bessa
Trindade,A
Monteiro,A
Vladimiro Miranda
Silva,CSP
Issue Date: 2014
Abstract: The growing penetration of solar power technology at low voltage (LV) level introduces new challenges in the distribution grid operation. Across the world, Distribution System Operators (DSO) are implementing the Smart Grid concept and one key function, in this new paradigm, is solar power forecasting. This paper presents a new forecasting framework, based on vector autoregression theory, that combines spatial-temporal data collected by smart meters and distribution transformer controllers to produce six-hour-ahead forecasts at the residential solar photovoltaic (PV) and secondary substation (i.e., MV/LV substation) levels. This framework has been tested for 44 micro-generation units and 10 secondary substations from the Smart Grid pilot in Évora, Portugal (one demonstration site of the EU Project SuSTAINABLE). A comparison was made with the well-known Autoregressive forecasting Model (AR-univariate model) leading to an improvement between 8% and 12% for the first 3 lead-times. © 2014 Power Systems Computation Conference.
URI: http://repositorio.inesctec.pt/handle/123456789/3540
http://dx.doi.org/10.1109/PSCC.2014.7038462
metadata.dc.type: conferenceObject
Publication
Appears in Collections:CPES - Articles in International Conferences

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