Sky image-based solar irradiance prediction methodologies using artificial neural networks

Abstract In order to decelerate global warming, it is important to promote renewable energy technologies. Solar energy, which is one of the most promising renewable energy sources, can be converted into electricity by using photovoltaic power generation systems. Whether the photovoltaic power generation systems are connected to an electrical grid or not, predicting near-future global solar radiation is useful to balance electricity supply and demand. In this work, two methodologies utilizing artificial neural networks (ANNs) to predict global horizontal irradiance in 1 to 5 minutes in advance from sky images are proposed. These methodologies do not require cloud detection techniques. Sky photo image data have been used to detect the clouds in the existing techniques, while color information at limited number of sampling points in the images are used in the proposed methodologies. The proposed methodologies are able to capture the trends of fluctuating solar irradiance with minor discrepancies. The minimum root mean square errors of 143 W/m2, which are comparable with the existing prediction techniques, are achieved for both of the methodologies. At the same time, the proposed methodologies require much less image data to be handled compared to the existing techniques.

[1]  A. Bais,et al.  The effect of clouds on surface solar irradiance, based on data from an all-sky imaging system , 2016 .

[2]  Soteris A. Kalogirou,et al.  Machine learning methods for solar radiation forecasting: A review , 2017 .

[3]  Alvy Ray Smith,et al.  Color gamut transform pairs , 1978, SIGGRAPH.

[4]  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 .

[5]  O. Şenkal.  Modeling of solar radiation using remote sensing and artificial neural network in Turkey , 2010 .

[6]  Josep Calbó,et al.  Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images , 2006 .

[7]  C. W. Chow,et al.  A method for cloud detection and opacity classification based on ground based sky imagery , 2012 .

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

[9]  Miguel-Ángel Manso-Callejo,et al.  Forecasting short-term solar irradiance based on artificial neural networks and data from neighboring meteorological stations , 2016 .

[10]  Carlos F.M. Coimbra,et al.  Forecasting of Global Horizontal Irradiance Using Sky Cover Indices , 2013 .

[11]  Gordon B. Davis,et al.  Automatic Estimation of Cloud Amount Using Computer Vision , 1992 .

[12]  Sumedha Rajakaruna,et al.  Very short-term photovoltaic power forecasting with cloud modeling: A review , 2017 .

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

[14]  Enio Bueno Pereira,et al.  A Simple Method for the Assessment of the Cloud Cover State in High-Latitude Regions by a Ground-Based Digital Camera , 2006 .

[15]  Dazhi Yang,et al.  Very short term irradiance forecasting using the lasso , 2015 .

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

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

[18]  Bri-Mathias Hodge,et al.  A suite of metrics for assessing the performance of solar power forecasting , 2015 .

[19]  Rasool Azimi,et al.  A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting , 2016 .

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

[21]  Jeff Sabburg,et al.  Evaluation of a Ground-Based Sky Camera System for Use in Surface Irradiance Measurement , 1999 .

[22]  Gordon Reikard.  Predicting solar radiation at high resolutions: A comparison of time series forecasts , 2009 .

[23]  Christian Riess,et al.  Continuous short-term irradiance forecasts using sky images , 2014 .

[24]  J. Alonso,et al.  Sky camera imagery processing based on a sky classification using radiometric data , 2014 .

[25]  F. S. Tymvios,et al.  Comparative study of Ångström's and artificial neural networks' methodologies in estimating global solar radiation , 2005 .

[26]  Ram Gopal Raj,et al.  The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review , 2015 .

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

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

[29]  Francisco J. Batlles,et al.  Solar irradiance forecasting at one-minute intervals for different sky conditions using sky camera images , 2015 .

[30]  Belkacem Draoui,et al.  Estimating Global Solar Radiation Using Artificial Neural Network and Climate Data in the South-western Region of Algeria , 2012 .

[31]  A. Regattieri,et al.  Artificial neural network optimisation for monthly average daily global solar radiation prediction , 2016 .

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

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

[34]  Miguel Angel Gonzalez-Salazar,et al.  Review of the operational flexibility and emissions of gas- and coal-fired power plants in a future with growing renewables , 2018 .

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