Sun-tracking imaging system for intra-hour DNI forecasts

A Sun-tracking imaging system is implemented for minimizing circumsolar image distortion for improved short-term solar irradiance forecasts. This sky-imaging system consists of a fisheye digital camera mounted on an automatic solar tracker that follows the diurnal pattern of the Sun. The Sun is located at the geometric center of the sky images where the fisheye distortion is minimized. Images from this new system provide more information about the circumsolar sky cover, which provides critical information for intra-hour solar forecasts, particularly for direct normal irradiance. An automatic masking algorithm has been developed to separate the sky area from ground obstacles and the image edges for each image that is collected. Then numerical image features are extracted from the segmented sky area and are used as exogenous inputs to MultiLayer Perceptron (MLP) models for direct normal irradiance forecasts. Sixty-seven days of irradiance and image measurements are used to train, optimize, and assess the MLP-based forecast models for solar irradiance. The results show that the MLP forecasts based on the newly proposed sky-imaging system significantly outperform the reference models in terms of statistical metrics and forecast skill, particularly for shorter horizons, achieving forecast skills 18%–50% higher than the skills of a reference MLP-based model that is based on a zenith-pointed, stationary sky-imaging system.

[1]  Carlos F.M. Coimbra,et al.  Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning , 2013 .

[2]  Daniel Rowe,et al.  Short-term irradiance forecasting using skycams: Motivation and development , 2014 .

[3]  Carlos F.M. Coimbra,et al.  Verification of the SUNY direct normal irradiance model with ground measurements , 2014 .

[4]  Jun Yang,et al.  A Hybrid Thresholding Algorithm for Cloud Detection on Ground-Based Color Images , 2011 .

[5]  C. Coimbra,et al.  Intra-hour DNI forecasting based on cloud tracking image analysis , 2013 .

[6]  L. Zarzalejo,et al.  Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning , 2010 .

[7]  Carlos F.M. Coimbra,et al.  Real-time prediction intervals for intra-hour DNI forecasts , 2015 .

[8]  T. Hoff,et al.  Validation of short and medium term operational solar radiation forecasts in the US , 2010 .

[9]  J. Kleissl,et al.  Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed , 2011 .

[10]  C. Coimbra,et al.  Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database , 2011 .

[11]  Jan Kleissl,et al.  Solar variability of four sites across the state of Colorado , 2010 .

[12]  Alexandros G. Charalambides,et al.  Enhanced values of global irradiance due to the presence of clouds in Eastern Mediterranean , 2014 .

[13]  Charles M. Bachmann,et al.  Neural Networks and Their Applications , 1994 .

[14]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for photovoltaic applications: A review , 2008 .

[15]  Girish Kumar Singh,et al.  Solar power generation by PV (photovoltaic) technology: A review , 2013 .

[16]  Jan Kleissl,et al.  Solar Energy Forecasting and Resource Assessment , 2013 .

[17]  P. Ineichen.  A broadband simplified version of the Solis clear sky model , 2008 .

[18]  Jiacong Cao,et al.  Study of hourly and daily solar irradiation forecast using diagonal recurrent wavelet neural networks , 2008 .

[19]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..

[20]  Hoay Beng Gooi,et al.  Solar radiation forecast based on fuzzy logic and neural networks , 2013 .

[21]  Carlos F.M. Coimbra,et al.  Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances , 2015 .

[22]  M. Richter,et al.  An Upper Threshold of Enhanced Global Shortwave Irradiance in the Troposphere Derived from Field Measurements in Tropical Mountains , 2008 .

[23]  R. Inman,et al.  Solar forecasting methods for renewable energy integration , 2013 .

[24]  H. Pedro,et al.  Assessment of forecasting techniques for solar power production with no exogenous inputs , 2012 .

[25]  C. Coimbra,et al.  Proposed Metric for Evaluation of Solar Forecasting Models , 2013 .

[26]  A. Massi Pavan,et al.  An adaptive model for predicting of global, direct and diffuse hourly solar irradiance , 2010 .

[27]  Wai Kean Yap,et al.  An off-grid hybrid PV/diesel model as a planning and design tool, incorporating dynamic and ANN modelling techniques , 2015 .

[28]  A. Hammer,et al.  Short-term forecasting of solar radiation: a statistical approach using satellite data , 1999 .

[29]  Sanjay Kumar,et al.  Application of system identification modelling to solar hybrid systems for predicting radiation, temperature and load , 2001 .

[30]  Adnan Sözen,et al.  Forecasting based on neural network approach of solar potential in Turkey , 2005 .

[31]  Carlos F.M. Coimbra,et al.  Cloud-tracking methodology for intra-hour DNI forecasting , 2014 .

[32]  Carlos F.M. Coimbra,et al.  Streamline-based method for intra-day solar forecasting through remote sensing , 2014 .

[33]  Amanpreet Kaur,et al.  Ensemble re-forecasting methods for enhanced power load prediction , 2014 .

[34]  Carlos F.M. Coimbra,et al.  A Smart Image-Based Cloud Detection System for Intrahour Solar Irradiance Forecasts , 2014 .

[35]  Carlos F.M. Coimbra,et al.  Real-time forecasting of solar irradiance ramps with smart image processing , 2015 .

[36]  A. Mellit,et al.  A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy , 2010 .

[37]  Carlos F.M. Coimbra,et al.  Short-term reforecasting of power output from a 48 MWe solar PV plant , 2015 .

[38]  Maher Chaabene,et al.  Neuro-fuzzy dynamic model with Kalman filter to forecast irradiance and temperature for solar energy systems , 2008 .

[39]  Carlos F.M. Coimbra,et al.  Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs , 2013 .

[40]  P. Ineichen,et al.  A new airmass independent formulation for the Linke turbidity coefficient , 2002 .

[41]  B. García-Domingo,et al.  CPV module electric characterisation by artificial neural networks , 2015 .

[42]  Carlos F.M. Coimbra,et al.  Forecasting of Global Horizontal Irradiance Using Sky Cover Indices , 2011 .