Development of a sky imaging system for short-term solar power forecasting

Urquhart, B.; Kurtz, Ben; Dahlin, E.; Ghonima, M. S.; Shields, Janet E.; Kleissl, Jan

doi:10.5194/amtd-7-4859-2014

Cited by 15 publications

(3 citation statements)

References 27 publications

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“…Systems in use range from commercially available low-cost web-cam-based sky cameras to highquality prototype systems developed at research institutes. These use digital cameras with charge-coupled device chips and large fish-eye lenses for photography and industry applications-for example, the sky imaging systems specifically designed for solar energy purposes at the University of California at San Diego (Urquhart et al 2014). Cloud detection from sky-imager pictures is performed by evaluating different image properties.…”

Section: Forecasting Using Ground-based Sky Imagersmentioning

confidence: 99%

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Sengupta¹,

Kurtz²,

Dobos³

et al. 2015

152

121

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ForewordThe first version of this handbook was developed in response to a growing need by the solar energy industry for a single document addressing the key aspects of solar resource characterization. The solar energy industry has developed rapidly throughout the last few years, and there have been significant enhancements in the body of knowledge in the areas of solar resource assessment and forecasting. Thus, this second version of the handbook was developed from the need to update and enhance the initial version and present the state of the art in a condensed form for all of its users.Although the first version of this handbook was developed by only researchers from the National Renewable Energy Laboratory, this version has additional contributions from an international group of experts primarily from the knowledge that has been gained through participation in the International Energy Agency's Solar Heating and Cooling Programme Task 36 and Task 46.As in the first version, this material was assembled by scientists and engineers who have many decades of combined experience in atmospheric science, radiometry, meteorological data processing, and renewable energy technology development.Readers are encouraged to provide feedback to the authors for future revisions and an expansion of the handbook's scope and content. iv This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. PrefaceAs the world looks for low-carbon sources of energy, solar power stands out as the single most abundant energy resource on Earth. Harnessing this energy is the challenge for this century. Photovoltaics, solar heating and cooling, and concentrating solar power (CSP) are primary forms of energy applications using sunlight. These solar energy systems use different technologies, collect different fractions of the solar resource, and have different siting requirements and production capabilities. Reliable information about the solar resource is required for every solar energy application. This holds true for small installations on a rooftop as well as for large solar power plants. However, solar resource information is of particular interest for large installations, because they require a substantial investment, sometimes exceeding $1 billion in construction costs. Before such a project is undertaken, the best possible information about the quality and reliability of the fuel source must be made available. That is, project developers need to have reliable data about the solar resource available at specific locations, including historic trends with seasonal, daily, hourly, and (preferably) subhourly variability to predict the daily and annual performance of a proposed power plant. Without this data, an accurate financial analysis is not possible.In September 2008, the U.S. Department of Energy (DOE) hosted a meeting of prominent CSP developers and stakeholders. One purpose was to identify areas in which the DOE's CSP program should focus its efforts to help the industry develop an...

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Section: Forecasting Using Ground-based Sky Imagersmentioning

confidence: 99%

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Sengupta¹,

Kurtz²,

Dobos³

et al. 2015

152

121

View full text Add to dashboard Cite

show abstract

“…Commercial sky imaging systems often prove too costly and have proprietary software, leading several research groups to develop their own systems. The solar forecasting research group at UC San Diego [25][26][27][28][29] has done pioneering work in this field for many years. For one example, Coimbra et al [30] proposed DNI forecasting models using images from a Yankee TSI 880 with hemispherical mirror as inputs to Artificial Neural Networks (ANN).…”

Section: State Of the Art In Solar Forecastingmentioning

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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“…This has led several groups to develop their own sky imaging systems for research purposes in solar forecasting. A large group at UC San Diego [13][14][15][16] has done pioneering work in this field. In [17], Coimbra et al…”

Section: State Of the Art In Solar Forecastingmentioning

confidence: 99%

A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting

Richardson

Krishnaswami

Vega³

et al. 2017

Sustainability

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Abstract: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.

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Development of a sky imaging system for short-term solar power forecasting

Cited by 15 publications

References 27 publications

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

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

A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting

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