Estimation methods for global solar radiation: Case study evaluation of five different approaches in central Spain

Urraca, Rubén; Martinez-De-Pison, E.; Sanz-García, Andrés; Antonanzas, J.; Antoñanzas-Torres, F.

doi:10.1016/j.rser.2016.11.222

Cited by 59 publications

(35 citation statements)

References 56 publications

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“…2018, 10, 1651; doi:10.3390/rs10101651 www.mdpi.com/journal/remotesensing High-resolution GHI data are required for studying those fields. Therefore, several studies have tried to estimate solar radiation (SR) and its components from either ground measurements or satellite images using several models [1][2][3][4][5][6]. The ground measurements of GHI have high accuracy and high temporal availability, whereas the high spatial resolution of recorded data and the number of stations with SR data are limited in most geographical areas.…”

Section: Introductionmentioning

confidence: 99%

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

et al. 2018

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Several sectors need global horizontal irradiance (GHI) data for various purposes. However, the availability of a long-term time series of high quality in situ GHI measurements is limited. Therefore, several studies have tried to estimate GHI by re-analysing climate data or satellite images. Validation is essential for the later use of GHI data in the regions with a scarcity of ground-recorded data. This study contributes to previous studies that have been carried out in the past to validate HelioClim-3 version 5 (HC3v5) and the Copernicus Atmosphere Monitoring Service, using radiation service version 3 (CRSv3) data of hourly GHI from satellite-derived datasets (SDD) with nine ground stations in northeast Iraq, which have not been used previously. The validation is carried out with station data at the pixel locations and two other data points in the vicinity of each station, which is something that is rarely seen in the literature. The temporal and spatial trends of the ground data are well captured by the two SDDs. Correlation ranges from 0.94 to 0.97 in all-sky and clear-sky conditions in most cases, while for cloudy-sky conditions, it is between 0.51–0.72 and 0.82–0.89 for the clearness index. The bias is negative for most of the cases, except for three positive cases. It ranges from −7% to 4%, and −8% to 3% for the all-sky and clear-sky conditions, respectively. For cloudy-sky conditions, the bias is positive, and differs from one station to another, from 16% to 85%. The root mean square error (RMSE) ranges between 12–20% and 8–12% for all-sky and clear-sky conditions, respectively. In contrast, the RMSE range is significantly higher in cloudy-sky conditions: above 56%. The bias and RMSE for the clearness index are nearly the same as those for the GHI for all-sky conditions. The spatial variability of hourly GHI SDD differs only by 2%, depending on the station location compared to the data points around each station. The variability of two SDDs is quite similar to the ground data, based on the mean and standard deviation of hourly GHI in a month. Having station data at different timescales and the small number of stations with GHI records in the region are the main limitations of this analysis.

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Section: Introductionmentioning

confidence: 99%

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

et al. 2018

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“…XGBoost is an extension of the Gradient Boosting Machine. The Boosting classifier belongs to the ensemble learning model (Hassan et al 2017;Urraca et al 2017). XGBoost is widely used in energy consumption prediction (Touzani et al 2018;Robinson et al 2017) and power distribution due to its high efficiency and accuracy.…”

Section: Evaluation Modelmentioning

confidence: 99%

“…However, the sample size was usually limited, and the existing methods greatly reduced the accuracy of the evaluation formula (Deng et al 2018;Jin et al 2017). In order to solve the above problems, the real store scene was built in the Key Laboratory of Building Environment Simulation to conduct an evaluation experiment; XGBoost (Hassan et al 2017;Urraca et al 2017) was used to solve the classification problem of big data. Based on this, the impact of lighting parameters on the lighting quality were studied, and lighting evaluation models were established to ensure visual health, to improve visual comfort, and to save lighting energy consumption, while satisfying the functions of lighting.…”

Section: Introductionmentioning

confidence: 99%

Lighting Quality Study of Shopping Malls in China Based on the Evaluation Experiment

Dang¹,

Bu²,

Wang³

et al. 2018

Healthy, Intelligent and Resilient Buildings and Urban Environments

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The lighting environment of the shopping malls will greatly influence the customer's visual comfort and product sales. This experiment studied the lighting environment of jewelry stores with the highest lighting energy consumption in shopping malls. The jewelry store scene which was built in the Key Laboratory of Building Environment Simulation had a total of 150 lighting conditions. Then, we measured lighting parameters, and subjective evaluation of lighting quality (including lighting comfort and commodities display effect) with 30 participants were carried out. We analyzed the experimental data to get the influence law of lighting parameters on lighting quality. Finally, this paper introduces an emerging ensemble learning algorithm XGBoost (extreme gradient boosting) to construct evaluation models of multiple lighting parameters for lighting quality in jewelry stores, which were verified to have good accuracy. It is of great significance to save energy on the basis of ensuring the lighting quality of the shopping malls.

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“…It relies on the semivariogram model and regionalized variable, and exists an underlying trend, this trend can be modeled as a function of the spatial coordinates. In this paper, the universal kriging algorithm provides more accurate interpolation results than other spatial interpolation method, and it is applied widely in the geological interpolation area and climate variables [21][22][23].…”

Section: Study Area and Datamentioning

confidence: 99%

Sunshine Duration Variability in Haihe River Basin, China, during 1966–2015

Ren

Lei

Zhang

et al. 2017

Water

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Sunshine can have a profound impact on the systematic change in climate elements, such as temperature and wind speed, and in turn affects many aspects of the human society. In recent years, there has been a substantial interest in the variation of sunshine duration due to the dramatic global climate change. Hence, there is a need to better understand the variation of sunshine duration in order to cope with climate change. This study aimed to analyze the variation of sunshine duration in Haihe River basin, China, and its relationship with temperature, wind speed and low-level cloudiness. The annual, seasonal and monthly changes of sunshine duration were analyzed based on the data collected from 33 meteorological stations over the Haihe River basin during 1966-2015. It is evident that the annual, seasonal and monthly sunshine duration shows a decreasing trend over time. In addition, the annual sunshine duration is lower with a higher climate tendency rate in the southern and eastern coastal regions than that in the northwestern regions. It is negatively correlated with temperature (r = −0.50) and low-level cloudiness (r = −0.29), but positively with wind speed (r = 0.61). Wind speed may be one of the important causes of the decrease of sunshine duration in the Haihe River basin during 1966-2015. These changes may have significant implications for the hydrological cycle in the area.

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Estimation methods for global solar radiation: Case study evaluation of five different approaches in central Spain

Cited by 59 publications

References 56 publications

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

Lighting Quality Study of Shopping Malls in China Based on the Evaluation Experiment

Sunshine Duration Variability in Haihe River Basin, China, during 1966–2015

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