Remote Sensing

Research

28 pages, 11229 KiB

Open AccessArticle

Direct Short-Term Forecast of Photovoltaic Power through a Comparative Study between COMS and Himawari-8 Meteorological Satellite Images in a Deep Neural Network

by Minho Kim, Hunsoo Song and Yongil Kim

Remote Sens. 2020, 12(15), 2357; https://doi.org/10.3390/rs12152357 - 22 Jul 2020

Cited by 7 | Viewed by 4431

Abstract

Meteorological satellite images provide crucial information on solar irradiation and weather conditions at spatial and temporal resolutions which are ideal for short-term photovoltaic (PV) power forecasts. Following the introduction of next-generation meteorological satellites, investigating their application on PV forecasts has become imminent. In [...] Read more.

Meteorological satellite images provide crucial information on solar irradiation and weather conditions at spatial and temporal resolutions which are ideal for short-term photovoltaic (PV) power forecasts. Following the introduction of next-generation meteorological satellites, investigating their application on PV forecasts has become imminent. In this study, Communications, Oceans, and Meteorological Satellite (COMS) and Himawari-8 (H8) satellite images were inputted in a deep neural network (DNN) model for 2 hour (h)- and 1 h-ahead PV forecasts. A one-year PV power dataset acquired from two solar power test sites in Korea was used to directly forecast PV power. H8 was used as a proxy for GEO-KOMPSAT-2A (GK2A), the next-generation satellite after COMS, considering their similar resolutions, overlapping geographic coverage, and data availability. In addition, two different data sampling setups were designed to implement the input dataset. The first setup sampled chronologically ordered data using a relatively more inclusive time frame (6 a.m. to 8 p.m. in local time) to create a two-month test dataset, whereas the second setup randomly sampled 25% of data from each month from the one-year input dataset. Regardless of the setup, the DNN model generated superior forecast performance, as indicated by the lowest normalized mean absolute error (NMAE) and normalized root mean squared error (NRMSE) results in comparison to that of the support vector machine (SVM) and artificial neural network (ANN) models. The first setup results revealed that the visible (VIS) band yielded lower NMAE and NRMSE values, while COMS was found to be more influential for 1 h-ahead forecasts. For the second setup, however, the difference in NMAE results between COMS and H8 was not significant enough to distinguish a clear edge in performance. Nevertheless, this marginal difference and similarity of the results suggest that both satellite datasets can be used effectively for direct short-term PV forecasts. Ultimately, the comparative study between satellite datasets as well as spectral bands, time frames, forecast horizons, and forecast models confirms the superiority of the DNN and offers insights on the potential of transitioning to applying GK2A for future PV forecasts. Full article

(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)

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17 pages, 2980 KiB

Open AccessArticle

Site-Adaptation of Modeled Solar Radiation Data: The SiteAdapt Procedure

by Carlos M. Fernández-Peruchena, Jesús Polo, Luis Martín and Luis Mazorra

Remote Sens. 2020, 12(13), 2127; https://doi.org/10.3390/rs12132127 - 02 Jul 2020

Cited by 18 | Viewed by 3911

Abstract

The adaptation of modeled solar radiation data with coincident ground measurements has become a standard practice of the industry, typically requested by financial institutions in the detailed solar resource assessments of solar projects. This practice mitigates the risk of solar projects, enhancing the [...] Read more.

The adaptation of modeled solar radiation data with coincident ground measurements has become a standard practice of the industry, typically requested by financial institutions in the detailed solar resource assessments of solar projects. This practice mitigates the risk of solar projects, enhancing the adequate solar plant design and reducing the uncertainty of its yield estimates. This work presents a procedure for improving the accuracy of modeled solar irradiance series through site-adaptation with coincident ground-based measurements relying on the use of a regression preprocessing followed by an empirical quantile mapping (eQM) correction. It was tested at nine sites in a wide range of latitudes and climates, resulting in significant improvements of statistical indicators of dispersion, distribution similarity and overall performance: relative bias is reduced on average from −1.8% and −2.3% to 0.1% and 0.3% for GHI and DNI, respectively; relative root mean square deviation is reduced on average from 17.9% and 34.9% to 14.6% and 29.8% for GHI and DNI, respectively; the distribution similarity is also improved after the site-adaptation (KSI is 3.5 and 3.9 times lower for GHI and DNI at hourly scale, respectively). The methodology is freely available as supplementary material and downloadable as R-package from SiteAdapt. Full article

(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)

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16 pages, 11830 KiB

Open AccessArticle

Improving Vertical Wind Speed Extrapolation Using Short-Term Lidar Measurements

by Alexander Basse, Lukas Pauscher and Doron Callies

Remote Sens. 2020, 12(7), 1091; https://doi.org/10.3390/rs12071091 - 29 Mar 2020

Cited by 4 | Viewed by 3127

Abstract

This study investigates how short-term lidar measurements can be used in combination with a mast measurement to improve vertical extrapolation of wind speed. Several methods are developed and analyzed for their performance in estimating the mean wind speed, the wind speed distribution, and [...] Read more.

This study investigates how short-term lidar measurements can be used in combination with a mast measurement to improve vertical extrapolation of wind speed. Several methods are developed and analyzed for their performance in estimating the mean wind speed, the wind speed distribution, and the energy yield of an idealized wind turbine at the target height of the extrapolation. These methods range from directly using the wind shear of the short-term measurement to a classification approach based on commonly available environmental parameters using linear regression. The extrapolation strategies are assessed using data of ten wind profiles up to 200 m measured at different sites in Germany. Different mast heights and extrapolation distances are investigated. The results show that, using an appropriate extrapolation strategy, even a very short-term lidar measurement can significantly reduce the uncertainty in the vertical extrapolation of wind speed. This observation was made for short as well as for very large extrapolation distances. Among the investigated methods, the linear regression approach yielded better results than the other methods. Integrating environmental variables into the extrapolation procedure further increased the performance of the linear regression approach. Overall, the extrapolation error in (theoretical) energy yield was decreased by around 50% to 70% on average for a lidar measurement of approximately one to two months depending on the extrapolation height and distance. The analysis of seasonal patterns revealed that appropriate extrapolation strategies can also significantly reduce the seasonal bias that is connected to the season during which the short-term measurement is performed. Full article

(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)

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22 pages, 36007 KiB

Open AccessArticle

Wind Farm Wakes from SAR and Doppler Radar

by Tobias Ahsbahs, Nicolai Gayle Nygaard, Alexander Newcombe and Merete Badger

Remote Sens. 2020, 12(3), 462; https://doi.org/10.3390/rs12030462 - 02 Feb 2020

Cited by 24 | Viewed by 5194

Abstract

We retrieve atmospheric wake characteristics at the wind farm Westermost Rough from Sentinel-1 Synthetic Aperture Radar (SAR) images. For the first time, co-located reference measurements of the full flow field around the wind farm are available from Doppler radars. One case with a [...] Read more.

We retrieve atmospheric wake characteristics at the wind farm Westermost Rough from Sentinel-1 Synthetic Aperture Radar (SAR) images. For the first time, co-located reference measurements of the full flow field around the wind farm are available from Doppler radars. One case with a reference measurement of up to 10 km downstream of the wind farms shows that SAR images depict the wake better close to the wind farm than further downstream. The comparison of two cases with similar wind speed and direction indicate that under unstable atmospheric stratification, we can retrieve the structure of the wake field close to the wind farm from SAR, while this was not possible for a case with stable stratification. We find that openly available Sentinel-1 image archives can be used to study the structure of wind farm wakes depending on the atmospheric stability conditions. From an average of twelve available co-located cases, we find that velocity deficits at the wind turbine hub height are 8% from Doppler radar measurements and 4% from SAR wind retrievals. Full article

(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)

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23 pages, 5762 KiB

Open AccessArticle

The Seamless Solar Radiation (SESORA) Forecast for Solar Surface Irradiance—Method and Validation

by Isabel Urbich, Jörg Bendix and Richard Müller

Remote Sens. 2019, 11(21), 2576; https://doi.org/10.3390/rs11212576 - 02 Nov 2019

Cited by 15 | Viewed by 3774

Abstract

Due to the integration of fluctuating weather-dependent energy sources into the grid, the importance of weather and power forecasts grows constantly. This paper describes the implementation of a short-term forecast of solar surface irradiance named SESORA (seamless solar radiation). It is based on [...] Read more.

Due to the integration of fluctuating weather-dependent energy sources into the grid, the importance of weather and power forecasts grows constantly. This paper describes the implementation of a short-term forecast of solar surface irradiance named SESORA (seamless solar radiation). It is based on the the optical flow of effective cloud albedo and available for Germany and parts of Europe. After the clouds are shifted by applying cloud motion vectors, solar radiation is calculated with SPECMAGIC NOW (Spectrally Resolved Mesoscale Atmospheric Global Irradiance Code), which computes the global irradiation spectrally resolved from satellite imagery. Due to the high spatial and temporal resolution of satellite measurements, solar radiation can be forecasted from 15 min up to 4 h or more with a spatial resolution of 0.05

^{\circ}

. An extensive validation of this short-term forecast is presented in this study containing two different validations based on either area or stations. The results are very promising as the mean RMSE (Root Mean Square Error) of this study equals 59 W/m

^{2}

(absolute bias = 42 W/m

^{2}

) after 15 min, reaches its maximum of 142 W/m

^{2}

(absolute bias = 97 W/m

^{2}

) after 165 min, and slowly decreases after that due to the setting of the sun. After a brief description of the method itself and the method of the validation the results will be presented and discussed. Full article

(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)

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15 pages, 1001 KiB

Open AccessArticle

Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer

by Peter Brugger, Fernando Carbajo Fuertes, Mohsen Vahidzadeh, Corey D. Markfort and Fernando Porté-Agel

Remote Sens. 2019, 11(19), 2247; https://doi.org/10.3390/rs11192247 - 26 Sep 2019

Cited by 16 | Viewed by 2653

Abstract

Accurate prediction of wind turbine wakes is important for more efficient design and operation of wind parks. Volumetric wake measurements of nacelle-mounted Doppler lidars are used to characterize the wake of a full-scale wind turbine and to validate an analytical wake model that [...] Read more.

Accurate prediction of wind turbine wakes is important for more efficient design and operation of wind parks. Volumetric wake measurements of nacelle-mounted Doppler lidars are used to characterize the wake of a full-scale wind turbine and to validate an analytical wake model that incorporates the effect of wind veer. Both, measurements and model prediction, show an elliptical and tilted spanwise cross-section of the wake in the presence of wind veer. The error between model and measurements is reduced compared to a model without the effect of wind veer. The characterization of the downwind velocity deficit development and wake growth is robust. The wake tilt angle can only be determined for elliptical wakes. Full article

(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)

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18 pages, 3136 KiB

Open AccessArticle

Atmospheric Transmittance Model Validation for CSP Tower Plants

by Natalie Hanrieder, Abdellatif Ghennioui, Ahmed Alami Merrouni, Stefan Wilbert, Florian Wiesinger, Manajit Sengupta, Luis Zarzalejo and Alexander Schade

Remote Sens. 2019, 11(9), 1083; https://doi.org/10.3390/rs11091083 - 07 May 2019

Cited by 13 | Viewed by 3445

Abstract

In yield analysis and plant design of concentrated solar power (CSP) tower plants, increased uncertainties are caused by the mostly unknown solar attenuation between the concentrating heliostat field and the receiver on top of the tower. This attenuation is caused mainly by aerosol [...] Read more.

In yield analysis and plant design of concentrated solar power (CSP) tower plants, increased uncertainties are caused by the mostly unknown solar attenuation between the concentrating heliostat field and the receiver on top of the tower. This attenuation is caused mainly by aerosol particles and water vapor. Various on-site measurement methods of atmospheric extinction in solar tower plants have been developed during recent years, but during resource assessment for distinct tower plant projects in-situ measurement data sets are typically not available. To overcome this lack of information, a transmittance model (TM) has been previously developed and enhanced by the authors to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity and barometric pressure measurements. Previously the model was only tested at one site. In this manuscript, the enhanced TM is validated for three sites (CIEMAT’s Plataforma Solar de Almería (PSA), Spain, Missour, Morocco (MIS) and Zagora, Morocco (ZAG)). As the strongest assumption in the TM is the vertical aerosol particle profile, three different approaches to describe the vertical profile are tested in the TM. One approach assumes a homogeneous aerosol profile up to 1 kilometer above ground, the second approach is based on LIVAS profiles obtained from Lidar measurements and the third approach uses boundary layer height (BLH) data of the European Centre for Medium-Range Weather Forecasts (ECMWF). The derived broadband transmittance for a slant range of 1 km (

T_{1 k m}

) time series is compared with a reference data set of on-site absorption- and broadband corrected

T_{1 k m}

derived from meteorological optical range (MOR) measurements for the temporal period between January 2015 and November 2017. The absolute mean bias error (MBE) for the TM’s

T_{1 k m}

using the three different aerosol profiles lies below 5% except for ZAG and one profile assumption. The MBE is close to 0 for PSA and MIS assuming a homogeneous extinction coefficient up to 1 km above ground. The root mean square error (RMSE) is around 5–6% for PSA and ZAG and around 7–8% for MIS. The TM performs better during summer months, during which more data points have been evaluated. This validation proves the applicability of the transmittance model for resource assessment at various sites. It enables the identification of a clear site with high

T_{1 k m}

with a high accuracy and provides an estimation of the

T_{1 k m}

for hazy sites. Thus it facilitates the decision if on-site extinction measurements are necessary. The model can be used to improve the accuracy of yield analysis of tower plants and allows the site adapted design. Full article

(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)

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22 pages, 7059 KiB

Open AccessArticle

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

by Bijan Nouri, Stefan Wilbert, Pascal Kuhn, Natalie Hanrieder, Marion Schroedter-Homscheidt, Andreas Kazantzidis, Luis Zarzalejo, Philippe Blanc, Sharad Kumar, Neeraj Goswami, Ravi Shankar, Roman Affolter and Robert Pitz-Paal

Remote Sens. 2019, 11(9), 1059; https://doi.org/10.3390/rs11091059 - 05 May 2019

Cited by 28 | Viewed by 4084

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)

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