Journal
SOLAR ENERGY
Volume 114, Issue -, Pages 91-104Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2015.01.024
Keywords
Sky imaging; Solar forecasting; Smart forecasts; Ramp forecasts; Cloud transport
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Funding
- California Public Utilities Commission (CPUC) under California Solar Initiative (CSI) Program
- U.S. National Science Foundation (NSF) EECS (EPAS) award [1201986]
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1201986] Funding Source: National Science Foundation
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We develop a standalone, real-time solar forecasting computational platform to predict one minute averaged solar irradiance ramps ten minutes in advance. This platform integrates cloud tracking techniques using a low-cost fisheye network camera and artificial neural network (ANN) algorithms, where the former is used to introduce exogenous inputs and the latter is used to predict solar irradiance ramps. We train and validate the forecasting methodology with measured irradiance and sky imaging data collected for a six-month period, and apply it operationally to forecast both global horizontal irradiance and direct normal irradiance at two separate locations characterized by different micro-climates (coastal and continental) in California. The performance of the operational forecasts is assessed in terms of common statistical metrics, and also in terms of three proposed ramp metrics, used to assess the quality of ramp predictions. Results show that the forecasting platform proposed in this work outperforms the reference persistence model for both locations. (C) 2015 Elsevier Ltd. All rights reserved.
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