Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area

Abstract

Nowadays, it is of great interest to know and forecast the solar energy resource that will be constantly available in order to optimize its use. The generation of electrical energy using CSP (concentrated solar power) plants is mostly affected by atmospheric changes. Therefore, forecasting solar irradiance is essential for planning a plant's operation. Solar irradiance/atmospheric (clouds) interaction studies using satellite and sky images can help to prepare plant operators for solar surface irradiance fluctuations. In this work, we present three methodologies that allow us to estimate direct normal irradiance (DNI). The study was carried out at the Solar Irradiance Observatory (SIO) at the Geophysics Institute (UNAM) in Mexico City using corresponding images obtained with a sky camera and starting from a clear sky model. The multiple linear regression and polynomial regression models as well as the neural networks model designed in the present study, were structured to work under all sky conditions (cloudy, partly cloudy and cloudless), obtaining estimation results with 82% certainty for all sky types. and overcast skies over the short-term (from 1 to 180 min) using a sky camera, where the average nRMSE values obtained were 24. 36%, 20.9% and 19.17%, respectively [5]. G. Reikard calculated the solar irradiance over time horizons of 60, 30, 15, and 5 min, implementing Autoregressive Integrated Moving Average (ARIMA) with errors between 20% and 90% [6]. Solar irradiance forecasting applied to photovoltaic energy production was implemented using the Smart Persistence algorithm in Machine Learning techniques, achieving an nRMSE of 25% on the best panels over short horizons, and 33% over a 6 h horizon [7].An analysis of energy forecasting in solar-tower plants combining a short-term solar irradiation forecasting scheme with a solar-tower plant model, the System Advisor Model (SAM), was used to simulate the behavior of the Gemasolar and Crescent Dunes plants. The findings showed that the best results appeared for the 90-min horizon, where the annual forecasting energy yield for Gemasolar was 97.34 GWh year while for Crescent Dunes it was 392.57 GWh year [8]. Similarly, cloud abundance forecasting has been studied for timescales of between (1-180 min), resulting in short-term forecasting (of less than one hour) and medium-term forecasting (up to 3 h), which was proven to have an 80% success rate-indeed, it was so successful that an application (portal) tool was developed that helps to increase power plant production [9,10].Recent studies have presented a method for the probabilistic forecasting of solar irradiance based on the joint Probability Distribution Function (PDF) of irradiance predicted using the Numerical Weather Prediction (NWP) and the irradiance observed; these are based on models of meteorological processes such as atmospheric dynamics, cloud formation and radiative transfer processes [11]. H. Yang and B. Kurtz estimated direct solar irradiance over the short and medium term for different sky conditions ...

Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing

“…Mondragón et al [2] presented an article focusing on forecasting the beam solar irradiance (DNI) to support the concentrated solar power environment. They developed three models for estimating the DNI attenuation percentage under all-sky conditions based on a sky camera with a rotational shadow band (Model TSI 880).…”

“…This Special Issue is comprised of ten research papers [1][2][3][4][5][6][7][8][9][10] and one letter [11]. Figure 1 shows a pictorial word cloud of the Abstract contents of the eleven manuscripts, where the strong multi-disciplinary character of the remote sensing approaches applied to renewable energy is apparent.…”

Day and Night Clouds Detection Using a Thermal-Infrared All-Sky-View Camera

“…Continuous clouds observation and the retrieved parameters can be applied to several research fields, such as solar energy prediction [1][2][3], performance evaluation of photovoltaic power generation [4], aviation/navigation, meteorology research, as well as atmospheric science and climate research [5][6][7][8]. Depending on the platform, cloud observation can be classified into space-based remote sensing [9,10] (e.g., satellite observation) and ground-based observation.…”

Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery

“…Cloud cover has been widely studied in different places around the world, above all to examine the role of clouds in solar radiation measurements [7][8][9][10]. Research studies have applied digitized image analysis to satellite images and sky cameras to study solar radiation estimation and solar radiation attenuation caused by the aerosol column and clouds; this has resulted in estimates with 82% certainty for all sky types [10][11][12][13].…”