The effect of clouds on surface solar irradiance, based on data from an all-sky imaging system

Tzoumanikas, Panagiotis; Nikitidou, E.; Bais, A. F.; Kazantzidis, Andreas

doi:10.1016/j.renene.2016.04.026

Cited by 71 publications

(41 citation statements)

References 33 publications

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“…The percentage of cloud cover affects the amount of solar radiation received, with solar radiation increasing when the sky is clear (Tzoumanikas et al 2016). Long daylight hours usually provide the potential for more solar radiation.…”

Section: Bioresourcescommentioning

confidence: 99%

Applying a GIS-based Fuzzy Method to Identify Suitable Locations for Solar Kilns

Phonetip¹,

Ozarska²,

Brodie³

et al. 2018

BioResources

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The aim of this study was to identify suitable locations for solar kilns in Vientiane, the capital of Laos, based on geographical and climatic conditions and restricted areas. The criteria of the parameters, which were incorporated with Fuzzy membership functions, were used to create layers in the ArcGIS environment to draw maps of suitability. Climatic parameters, based on the Fuzzy method, were used to investigate the period of productive performance for solar kilns. The results showed a range of possible locations. The most suitable locations were in flat areas near roads. They were far from protected areas, rivers, and flood prone areas. The most productive performance period for operating solar kilns was from November until May.

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Section: Bioresourcescommentioning

confidence: 99%

Applying a GIS-based Fuzzy Method to Identify Suitable Locations for Solar Kilns

Phonetip¹,

Ozarska²,

Brodie³

et al. 2018

BioResources

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“…Commonly used cloud segmentation approaches use machine learning (e.g., [7,8]) while others use fixed (e.g., [9,10]) or variable (e.g., [11]) thresholds. Further important processing steps for nowcasting systems are the cloud tracking (e.g., [12,13]) and an analysis of the current and future radiative effect (e.g., [14,15]) of the clouds. Cloud height is not needed if the nowcast targets only the area directly next to the ASI.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts

Nouri¹,

Wilbert²,

Kuhn³

et al. 2019

Remote Sensing

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The incoming downward shortwave solar irradiance is harvested to an increasing extent by solar power plants. However, the variable nature of this energy source poses an operational challenge for solar power plants and electrical grids. Intra hour solar irradiance nowcasts with a high temporal and spatial resolution could be used to tackle this challenge. All sky imager (ASI) based nowcasting systems fulfill the requirements in terms of temporal and spatial resolution. However, ASI nowcasts can only be used if the required accuracies for applications in solar power plants and electrical grids are fulfilled. Scalar error metrics, such as mean absolute deviation, root mean square deviation, and skill score are commonly used to estimate the accuracy of nowcasting systems. However, these overall error metrics represented by a single number per metric are neither suitable to determine the real time accuracy of a nowcasting system in the actual weather situation, nor suitable to describe any spatially resolved nowcast accuracy. The performance of ASI-based nowcasting systems is strongly related to the prevailing weather conditions. Depending on weather conditions, large discrepancies between the overall and current system uncertainties are conceivable. Furthermore, the nowcast accuracy varies strongly within the irradiance map as higher errors may occur at transient zones close to cloud shadow edges. In this paper, we present a novel approach for the spatially resolved real-time uncertainty specification of ASI-based nowcasting systems. The current irradiance conditions are classified in one of eight distinct temporal direct normal irradiance (DNI) variability classes. For each class and lead-time, an upper and lower uncertainty value is derived from historical data, which describes a coverage probability of 68.3%. This database of uncertainty values is based on deviations of the irradiance maps, compared to three reference pyrheliometers in Tabernas, Andalucia over two years (2016 and 2017). Increased uncertainties due to transient effects are considered by detecting transient zones close to cloud shadow edges within the DNI map. The width of the transient zones is estimated by the current average cloud height, cloud speed, lead-time, and Sun position. The final spatially resolved uncertainties are validated with three reference pyrheliometers, using a data set consisting of the entire year 2018. Furthermore, we developed a procedure based on the DNI temporal variability classes to estimate the expected average uncertainties of the nowcasting system at any geographical location. The novel method can also be applied for global tilted or horizontal irradiance and is assumed to improve the applicability of the ASI nowcasts.

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“…Various kinds of features can be studied from sky images, such as the area of a sky region [12] and the motion vector field of clouds [14]. Clouds have been found to be the major factor in the atmosphere system that affects the incoming solar energy [13][14][15]. In fact, the fluctuation of solar radiation is caused mainly by clouds.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, the fluctuation of solar radiation is caused mainly by clouds. When sunlight is blocked by clouds, the clouds absorb and scatter the shortwave part of the radiation spectrum and absorb the long-wave part and reemit it downwards and into space, thus decreasing the amount of solar radiation that hits the ground [13]. As such, most sky image based methods focus on the extraction of cloud information, e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solar Radiation Forecast Using Cloud Velocity for Photovoltaic Systems

Sing

Ken

Yee

et al. 2018

J. Eng. Technol. Sci.

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Today, solar energy is used in a many different ways. One of the most popular technological developments for this purpose is photovoltaic conversion to electricity. However, power fluctuations due to the variability of solar energy are one of the challenges faced by the implementation of photovoltaic systems. To overcome this problem, forecasting solar radiation data several minutes in advance is needed. In this research, a methodology to forecast solar radiation using cloud velocity and cloud moving angle is proposed. Generally, a red-toblue ratio (RBR) color model and correlation analysis are used for obtaining the cloud velocity and moving angle. Artificial neural network (ANN) forecast models with different input combinations are established. This methodology requires lower computational time since it only uses part of the pixels in the sky image. Based on R-squared analysis, it can be concluded that the ANN model with inputs of cloud velocity and moving angle and average solar radiation showed the highest accuracy among other combinations of inputs. The R-squared value was 0.59 with only a relatively small sample size of 42. The proposed model showed a highest improvement of 75.79% when compared to the ANN model based on historical solar radiation data only.

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The effect of clouds on surface solar irradiance, based on data from an all-sky imaging system

Cited by 71 publications

References 33 publications

Applying a GIS-based Fuzzy Method to Identify Suitable Locations for Solar Kilns

Applying a GIS-based Fuzzy Method to Identify Suitable Locations for Solar Kilns

Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts

Solar Radiation Forecast Using Cloud Velocity for Photovoltaic Systems

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