First Results of a Low Cost All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting Serving a PV-Based Smart Grid in La Graciosa Island

Cañadillas, David; Richardson, W. B.; González‐Díaz, Benjamín; Shephard, Les E.; Lemus, Ricardo Guerrero

doi:10.1109/pvsc.2017.8366108

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Several previous papers [2,3,5,6] have detailed the development and deployment of the low cost, edge-computing UTSA SkyImager (Patent Pending). Figure 3 shows the SkyImager deployed at four locations: NREL in Golden, CO, Joint Base San Antonio, and two locations in the Canary Islands.…”

Section: Utsa Skyimagermentioning

confidence: 99%

Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset

Moncada

Richardson

Vega-Avila³

2018

Energies

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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.

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Section: Utsa Skyimagermentioning

confidence: 99%

Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset

Moncada

Richardson

Vega-Avila³

2018

Energies

Self Cite

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“…It serves as a case study in managing research in a joint theatre of operations and integrating the efforts of researchers and engineers who came from very different university and industrial cultures. Details of our research and technology development have been presented in journal articles [8,9], conference proceedings [10][11][12][13], and technical reports [14].…”

Section: Introductionmentioning

confidence: 99%

Validation of All-Sky Imager Technology and Solar Irradiance Forecasting at Three Locations: NREL, San Antonio, Texas, and the Canary Islands, Spain

et al. 2019

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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.

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Internet of Things System for Solar Resource Monitoring

Voicu,

Petreus,

Etz

2023

2023 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) &Amp; 2023 International Conference O

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First Results of a Low Cost All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting Serving a PV-Based Smart Grid in La Graciosa Island

Cited by 3 publications

References 7 publications

Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset

Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset

Validation of All-Sky Imager Technology and Solar Irradiance Forecasting at Three Locations: NREL, San Antonio, Texas, and the Canary Islands, Spain

Internet of Things System for Solar Resource Monitoring

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