This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conver- sion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind speed, in order to estimate the power produced by a PV plant at the grid connection terminals. The forecast values are obtained using a spatio-temporal method that uses the data recorded from a target meteorological station as well as data of its surrounding stations. The proposed forecasting method exploits the sparsity of correlations between time series data in a collection of sta- tions. The performance of both the PV conversion model and the spatio-temporal algorithm is evaluated using high-resolution real data recorded in various locations in Italy. Comparison with other benchmark methods illustrates that the proposed method significantly improves the solar power forecasts, particularly over short-term horizons.

Compressive Spatio-Temporal Forecasting of Meteorological Quantities and Photovoltaic Power / Tascikaraoglu, Akin; Sanandaji, Borhan M.; Chicco, Gianfranco; Cocina, VALERIA CONCETTA; Spertino, Filippo; Erdinc, Ozan; Paterakis, Nikolaos G.; Catalão, João P. S.. - In: IEEE TRANSACTIONS ON SUSTAINABLE ENERGY. - ISSN 1949-3029. - STAMPA. - 7:3(2016), pp. 1295-1305. [10.1109/TSTE.2016.2544929]

Compressive Spatio-Temporal Forecasting of Meteorological Quantities and Photovoltaic Power

CHICCO, GIANFRANCO;COCINA, VALERIA CONCETTA;SPERTINO, Filippo;
2016

Abstract

This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conver- sion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind speed, in order to estimate the power produced by a PV plant at the grid connection terminals. The forecast values are obtained using a spatio-temporal method that uses the data recorded from a target meteorological station as well as data of its surrounding stations. The proposed forecasting method exploits the sparsity of correlations between time series data in a collection of sta- tions. The performance of both the PV conversion model and the spatio-temporal algorithm is evaluated using high-resolution real data recorded in various locations in Italy. Comparison with other benchmark methods illustrates that the proposed method significantly improves the solar power forecasts, particularly over short-term horizons.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2646320