Short-term forecasting based on all-sky cameras

Kazantzidis, Andreas; Tzoumanikas, Panagiotis; Blanc, Philippe; Massip, Pierre; Wilbert, Stefan; Santigosa, Lourdes Ramirez

doi:10.1016/b978-0-08-100504-0.00005-6

Cited by 16 publications

(15 citation statements)

References 25 publications

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“…The data shown covers the period on 13 July 2020. from 13:45 to 14:40 and from 12:00 to 12:45. Figures [12][13][14][15][16][17][18][19] show the situation in the sky where several clouds were moving towards the sun and corresponding error levels. The red curve, which represents the real-time sun un-coverage level on images that compare the real-time and predicted sun-uncoverage level, shows that the visible area of the sun changes significantly in a nonlinear manner as the clouds begin to cover or uncover the sun.…”

Section: Resultsmentioning

confidence: 99%

“…Other methods used cloud motion prediction based on satellite images and motion vectors to predict output power, but only 30 min in advance, which may be a limitation, especially when some power storage devices in the microgrid system need more time to change their operating mode from charging to discharging [12]. A similar method has been used with several fisheye cameras but could predict up to 15 to 30 min of updates per minute [13], but our method manages to predict solar coverage up to 1 h.…”

Section: Introductionmentioning

confidence: 99%

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Solar Irradiance Forecast Based on Cloud Movement Prediction

Radovan¹,

Šunde

Kučak

et al. 2021

Energies

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Solar energy production based on a photovoltaic system is closely related to solar irradiance. Therefore, the planning of production is based on the prediction of solar irradiance. The optimal use of different energy storage systems requires an accurate prediction of solar irradiation with at least an hourly time horizon. In this work, a solar irradiance prediction method is developed based on the prediction of solar shading by clouds. The method is based on determining the current cloud position and estimating the velocity from a sequence of multiple images taken with a 180-degree wide-angle camera with a resolution of 5 s. The cloud positions for the next hour interval are calculated from the estimated current cloud position and velocity. Based on the cloud position, the percentage of solar overshadowing by clouds is determined, i.e., the solar overshadowing curve for the next hour interval is calculated. The solar irradiance is determined by normalizing the percentage of the solar unshadowing curve to the mean value of the irradiance predicted by the hydrometeorological institute for that hourly interval. Image processing for cloud detection and localization is performed using a computer vision library and the Java programming language. The algorithm developed in this work leads to improved accuracy and resolution of irradiance prediction for the next hour interval. The predicted irradiance curve can be used as a predicted reference for solar energy production in energy storage system optimization.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solar Irradiance Forecast Based on Cloud Movement Prediction

Radovan¹,

Šunde

Kučak

et al. 2021

Energies

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“…Recent research in this area has focused on scaling‐up these methodologies to be able to incorporate many spatial locations (Cavalcante, Bessa, Reis, & Browell, ; Messner & Pinson, ), and conditioning statistical model on large scale weather regimes for wind energy applications (Browell, Drew, & Philippopoulos, ) or cloud regimes for solar (McCandless, Haupt, & Young, ). Augmenting power production data with remote sensing is a well‐established strategy for improving solar power forecast performance via incorporation of satellite imagery (Blanc, Remund, & Vallance, ) for hours‐ahead forecasting and sky cameras (Chow et al, ; Kazantzidis et al, ) for intrahour forecasting. Similar methods are beginning to emerge in wind power forecasting with the use of LIDAR and RADAR technology to observe and advect changes in wind speed as they approach a wind farm (Trombe et al, ; Valldecabres, Nygaard, Vera‐Tudela, von Bremen, & Kühn, ; Valldecabres, Peña, Courtney, von Bremen, & Kühn, ; Würth et al, ).…”

Section: Forecasting Renewable Energy Todaymentioning

confidence: 99%

The future of forecasting for renewable energy

Sweeney

Bessa

Browell

et al. 2019

WIREs Energy & Environment

149

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Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state‐of‐the‐art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Wind Power > Systems and Infrastructure Photovoltaics > Systems and Infrastructure

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“…Such spatially resolved DNI information can be provided by camera based monitoring and nowcasting systems. These systems provide an intra-minute temporal resolution and a spatial resolution ≤ 20 m. The most common nowcasting systems consist of upward-facing all sky imagers (ASI) (Chow et al 2011;Quesada-Ruiz et al 2014;Peng et al 2015;Blanc et al 2017;Kazantzidis et al 2017;Nouri et al 2019b). The principle method of these ASI systems is most often similar.…”

Section: Introductionmentioning

confidence: 99%