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Global horizontal irradiance (i.e., shortwave downward solar radiation received by a horizontal surface on the ground) is an important geophysical variable for climate and energy research. Since solar radiation is attenuated by clouds, its variability is intimately associated with the variability of cloud properties. The spatial distribution of clouds and the daily, monthly, seasonal, and annual solar energy potential (i.e., the solar energy available to be converted into electricity) derived from satellite estimates of global horizontal irradiance are explored over the state of Texas, USA and surrounding regions, including northern Mexico and the western Gulf of Mexico. The maximum (minimum) monthly solar energy potential in the study area is 151–247 kWhm−2 (43–145 kWhm−2) in July (December). The maximum (minimum) seasonal solar energy potential is 457–706 kWhm−2 (167–481 kWhm−2) in summer (winter). The available annual solar energy in 2015 was 1295–2324 kWhm−2. The solar energy potential is significantly higher over the Gulf of Mexico than over land despite the ocean waters having typically more cloudy skies. Cirrus is the dominant cloud type over the Gulf which attenuates less solar irradiance compared to other cloud types. As expected from our previous work, there is good agreement between satellite and ground estimates of solar energy potential in San Antonio, Texas, and we assume this agreement applies to the surrounding larger region discussed in this paper. The study underscores the relevance of geostationary satellites for cloud/solar energy mapping and provides useful estimates on solar energy in Texas and surrounding regions that could potentially be harnessed and incorporated into the electrical grid.
Shuang Xia; Alberto M. Mestas-Nuñez; Hongjie Xie; Rolando Vega. Satellite-based Cloudiness and Solar Energy Potential in Texas and Surrounding Regions. Remote Sensing 2019, 11, 1130 .
AMA StyleShuang Xia, Alberto M. Mestas-Nuñez, Hongjie Xie, Rolando Vega. Satellite-based Cloudiness and Solar Energy Potential in Texas and Surrounding Regions. Remote Sensing. 2019; 11 (9):1130.
Chicago/Turabian StyleShuang Xia; Alberto M. Mestas-Nuñez; Hongjie Xie; Rolando Vega. 2019. "Satellite-based Cloudiness and Solar Energy Potential in Texas and Surrounding Regions." Remote Sensing 11, no. 9: 1130.
Increasing photovoltaic (PV) generation in the world’s power grid necessitates accurate solar irradiance forecasts to ensure grid stability and reliability. The University of Texas at San Antonio (UTSA) SkyImager was designed as a low cost, edge computing, all-sky imager that provides intra-hour irradiance forecasts. The SkyImager utilizes a single board computer and high-resolution camera with a fisheye lens housed in an all-weather enclosure. General Purpose IO pins allow external sensors to be connected, a unique aspect is the use of only open source software. Code for the SkyImager is written in Python and calls libraries such as OpenCV, Scikit-Learn, SQLite, and Mosquito. The SkyImager was first deployed in 2015 at the National Renewable Energy Laboratory (NREL) as part of the DOE INTEGRATE project. This effort aggregated renewable resources and loads into microgrids which were then controlled by an Energy Management System using the OpenFMB Reference Architecture. In 2016 a second SkyImager was installed at the CPS Energy microgrid at Joint Base San Antonio. As part of a collaborative effort between CPS Energy, UT San Antonio, ENDESA, and Universidad de La Laguna, two SkyImagers have also been deployed in the Canary Islands that utilize stereoscopic images to determine cloud heights. Deployments at three geographically diverse locations not only provided large amounts of image data, but also operational experience under very different climatic conditions. This resulted in improvements/additions to the original design: weatherproofing techniques, environmental sensors, maintenance schedules, optimal deployment locations, OpenFMB protocols, and offloading data to the cloud. Originally, optical flow followed by ray-tracing was used to predict cumulus cloud shadows. The latter problem is ill-posed and was replaced by a machine learning strategy with impressive results. R2 values for the multi-layer perceptron of 0.95 for 5 moderately cloudy days and 1.00 for 5 clear sky days validate using images to determine irradiance. The SkyImager in a distributed environment with cloud-computing will be an integral part of the command and control for today’s SmartGrid and Internet of Things.
Walter Richardson; David Cañadillas; Ariana Moncada; Ricardo Guerrero-Lemus; Les Shephard; Rolando Vega-Avila; Hariharan Krishnaswami. Validation of All-Sky Imager Technology and Solar Irradiance Forecasting at Three Locations: NREL, San Antonio, Texas, and the Canary Islands, Spain. Applied Sciences 2019, 9, 684 .
AMA StyleWalter Richardson, David Cañadillas, Ariana Moncada, Ricardo Guerrero-Lemus, Les Shephard, Rolando Vega-Avila, Hariharan Krishnaswami. Validation of All-Sky Imager Technology and Solar Irradiance Forecasting at Three Locations: NREL, San Antonio, Texas, and the Canary Islands, Spain. Applied Sciences. 2019; 9 (4):684.
Chicago/Turabian StyleWalter Richardson; David Cañadillas; Ariana Moncada; Ricardo Guerrero-Lemus; Les Shephard; Rolando Vega-Avila; Hariharan Krishnaswami. 2019. "Validation of All-Sky Imager Technology and Solar Irradiance Forecasting at Three Locations: NREL, San Antonio, Texas, and the Canary Islands, Spain." Applied Sciences 9, no. 4: 684.
Since the main attenuation of solar irradiance reaching the earth’s surface is due to clouds, it has been hypothesized that global horizontal irradiance attenuation and its temporal variability at a given location could be characterized simply by cloud properties at that location. This hypothesis is tested using global horizontal irradiance measurements at two stations in San Antonio, Texas, and satellite estimates of cloud types and cloud layers from the Geostationary Operational Environmental Satellite (GOES) Surface and Insolation Product. A modified version of an existing solar attenuation variability index, albeit having a better physical foundation, is used. The analysis is conducted for different cloud conditions and solar elevations. It is found that under cloudy-sky conditions, there is less attenuation under water clouds than those under opaque ice clouds (optically thick ice clouds) and multilayered clouds. For cloud layers, less attenuation was found for the low/mid layers than for the high layer. Cloud enhancement occurs more frequently for water clouds and less frequently for mixed phase and cirrus clouds and it occurs with similar frequency at all three levels. The temporal variability of solar attenuation is found to decrease with an increasing temporal sampling interval and to be largest for water clouds and smallest for multilayered and partly cloudy conditions. This work presents a first step towards estimating solar energy potential in the San Antonio area indirectly using available estimates of cloudiness from GOES satellites.
Shuang Xia; Alberto M. Mestas-Nuñez; Hongjie Xie; Jiakui Tang; Rolando Vega. Characterizing Variability of Solar Irradiance in San Antonio, Texas Using Satellite Observations of Cloudiness. Remote Sensing 2018, 10, 2016 .
AMA StyleShuang Xia, Alberto M. Mestas-Nuñez, Hongjie Xie, Jiakui Tang, Rolando Vega. Characterizing Variability of Solar Irradiance in San Antonio, Texas Using Satellite Observations of Cloudiness. Remote Sensing. 2018; 10 (12):2016.
Chicago/Turabian StyleShuang Xia; Alberto M. Mestas-Nuñez; Hongjie Xie; Jiakui Tang; Rolando Vega. 2018. "Characterizing Variability of Solar Irradiance in San Antonio, Texas Using Satellite Observations of Cloudiness." Remote Sensing 10, no. 12: 2016.
Distributed PV power generation necessitates both intra-hour and day-ahead forecasting of solar irradiance. The UTSA SkyImager is an inexpensive all-sky imaging system built using a Raspberry Pi computer with camera. Reconfigurable for different operational environments, it has been deployed at the National Renewable Energy Laboratory (NREL), Joint Base San Antonio, and two locations in the Canary Islands. The original design used optical flow to extrapolate cloud positions, followed by ray-tracing to predict shadow locations on solar panels. The latter problem is mathematically ill-posed. This paper details an alternative strategy that uses artificial intelligence (AI) to forecast irradiance directly from an extracted subimage surrounding the sun. Several different AI models are compared including Deep Learning and Gradient Boosted Trees. Results and error metrics are presented for a total of 147 days of NREL data collected during the period from October 2015 to May 2016.
Ariana Moncada; Jr. Walter Richardson; Rolando Vega-Avila. Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset. Energies 2018, 11, 1988 .
AMA StyleAriana Moncada, Jr. Walter Richardson, Rolando Vega-Avila. Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset. Energies. 2018; 11 (8):1988.
Chicago/Turabian StyleAriana Moncada; Jr. Walter Richardson; Rolando Vega-Avila. 2018. "Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset." Energies 11, no. 8: 1988.
Estimates of solar irradiance at the earth’s surface from satellite observations are useful for planning both the deployment of distributed photovoltaic systems and their integration into electricity grids. In order to use surface solar irradiance from satellites for these purposes, validation of its accuracy against ground observations is needed. In this study, satellite estimates of surface solar irradiance from Geostationary Operational Environmental Satellite (GOES) are compared with ground observations at two sites, namely the main campus of the University of Texas at San Antonio (UTSA) and the Alamo Solar Farm of San Antonio (ASF). The comparisons are done mostly on an hourly timescale, under different cloud conditions classified by cloud types and cloud layers, and at different solar zenith angle intervals. It is found that satellite estimates and ground observations of surface solar irradiance are significantly correlated (p < 0.05) under all sky conditions (r: 0.80 and 0.87 on an hourly timescale and 0.94 and 0.91 on a daily timescale, respectively for the UTSA and ASF sites); on the hourly timescale, the correlations are 0.77 and 0.86 under clear-sky conditions, and 0.74 and 0.84 under cloudy conditions, respectively for the UTSA and ASF sites, and mostly >0.60 under different cloud types and layers for both sites. The correlations under cloudy-sky conditions are mostly stronger than those under clear-sky conditions at different solar zenith angles. The correlation coefficients are mostly the smallest with solar zenith angle in the range of 75–90° under all sky, clear-sky and cloudy-sky conditions. At the ASF site, the overall bias of GOES surface solar irradiance is small (+1.77 Wm−2) under all sky while relatively larger under clear-sky (−22.29 Wm−2) and cloudy-sky (+40.31 Wm−2) conditions. The overall good agreement of the satellite estimates with the ground observations underscores the usefulness of the GOES surface solar irradiance estimates for solar energy studies in the San Antonio area.
Shuang Xia; Alberto M. Mestas-Nuñez; Hongjie Xie; Rolando Vega. An Evaluation of Satellite Estimates of Solar Surface Irradiance Using Ground Observations in San Antonio, Texas, USA. Remote Sensing 2017, 9, 1268 .
AMA StyleShuang Xia, Alberto M. Mestas-Nuñez, Hongjie Xie, Rolando Vega. An Evaluation of Satellite Estimates of Solar Surface Irradiance Using Ground Observations in San Antonio, Texas, USA. Remote Sensing. 2017; 9 (12):1268.
Chicago/Turabian StyleShuang Xia; Alberto M. Mestas-Nuñez; Hongjie Xie; Rolando Vega. 2017. "An Evaluation of Satellite Estimates of Solar Surface Irradiance Using Ground Observations in San Antonio, Texas, USA." Remote Sensing 9, no. 12: 1268.
With increasing use of photovoltaic (PV) power generation by utilities and their residential customers, the need for accurate intra-hour and day-ahead solar irradiance forecasting has become critical. This paper details the development of a low cost all-sky imaging system and an intra-hour cloud motion prediction methodology that produces minutes-ahead irradiance forecasts. The SkyImager is designed around a Raspberry Pi single board computer (SBC) with a fully programmable, high resolution Pi Camera, housed in a durable all-weather enclosure. Our software is written in Python 2.7 and utilizes the open source computer vision package OpenCV. The SkyImager can be configured for different operational environments and network designs, from a standalone edge computing model to a fully integrated node in a distributed, cloud-computing based micro-grid. Preliminary results are presented using the imager on site at the National Renewable Energy Laboratory (NREL) in Golden, CO, USA during the fall of 2015 under a variety of cloud conditions.
Walter Richardson; Hariharan Krishnaswami; Rolando Vega; Michael Cervantes. A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting. Sustainability 2017, 9, 482 .
AMA StyleWalter Richardson, Hariharan Krishnaswami, Rolando Vega, Michael Cervantes. A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting. Sustainability. 2017; 9 (4):482.
Chicago/Turabian StyleWalter Richardson; Hariharan Krishnaswami; Rolando Vega; Michael Cervantes. 2017. "A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting." Sustainability 9, no. 4: 482.