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Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12008/21607 Cómo citar
Título: Intra hour forecasting for a 50 MW photovoltaic system in Uruguay : a baseline approach
Autor: Theocharides, Spyros
Alonso-Suárez, Rodrigo
Giacosa, Gianina
Makrides, George
Theristis, Marios
Georghiou, George E
Tipo: Preprint
Fecha de publicación: 2019
Resumen: The increased penetration of photovoltaic (PV) generation introduces new challenges for the stability of electricity grids. In this work, machine learning (ML) techniques were implemented to forecast PV power production up to 1-hour ahead with a 10-minute granularity. Three different input combinations were utilised: Model 1 (M1) using the AC power only, Model 2 (M2) using the elevation angle (α), azimuth angle (φ) and AC power and Model 3 (M3) using the AC power, α, φ and satellite observations (SAT) aiming to improve the forecasting performance. Historical PV operational data are used for the training and validation stages of intra-hour PV forecasting models for time t + 10 to 60 minutes ahead. The results obtained over the test set period (15% of the data, i.e. ≈ 110 days) have shown that M2 exhibits the best-performance with a normalised root mean square error (nRMSE) varying between 7.6% to 14.2%, whereas the skill score (SS) ranged between 6.5% and 30.9% for the 10- to 60-minute ahead respectively.
Descripción: Preprint. Trabajo presentado a la 46th IEEE PV Specialist Conference, 16-22 Junio 2019, Chicago, USA
Citación: Theocharides, S, Alonso-Suarez, R, Giacosa, G, y otros."Intra hour forecasting for a 50 MW photovoltaic system in Uruguay : a baseline approach" [Preprint] Publicado en las Actas de la 46th IEEE PV Specialist Conference, Chicago, USA, 2019.
Licencia: Licencia Creative Commons Atribución – No Comercial – Sin Derivadas (CC - By-NC-ND)
Aparece en las colecciones: Publicaciones académicas y científicas - Laboratorio de Energía Solar (LES)

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