High frequency generation of coupled GHI and DNI based on clustered Dynamic Paths

Peruchena, Carlos F.; Larrañeta, M. G.; Blanco, Manuel; Bernardos, Ana M.

doi:10.1016/j.solener.2017.11.024

Cited by 19 publications

(5 citation statements)

References 17 publications

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“…As future work and on the basis of this approach, one could treat systematic errors differently from occasional ones (such as forest fires or extreme sand storms). Other future works can focus on more detailed separation of solar irradiance data through the consideration of coupled GHI and DNI values [48], as well as more sophisticated techniques to adapt them. In addition, the relative weight and homogeneity of some model predictor's values along with the search of scaled distributions derived from solar irradiance which are similar for different sites [49,50] could lead to extending the approach presented in this work to regional (not only site) adaptation at high-frequency time scales.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…A large body of models have been proposed in literature, they are predominantly temporal only methodologies with some notable spatio-temporal models (Bright et al, 2017(Bright et al, , 2015Bright, 2019;Grantham et al, 2017;Ngoko et al, 2014;Munkhammar et al, 2017;Munkhammar and Widén, 2016, 2019Peruchena et al, 2018Peruchena et al, , 2015Zhang et al, 2018;Shi et al, 2018;Polo et al, 2011;Larrañeta et al, 2018). For instance, Peruchena et al (2018) presented a clustered-based methodology for the generation of 1-min coupled global horizontal irradiance (GHI) and direct normal irradiance (DNI) temporal time series on the basis of the envelope clear sky and Dynamic Paths concepts. Larrañeta et al (2018) proposed a geographically flexible methodology to synthetically downscale DNI timeseries from 1-hour to 1-min temporal resolution.…”

Section: An Overview Of Literaturementioning

confidence: 99%

Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data

Frimane

Soubdhan²,

Bright

et al. 2019

Solar Energy

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High resolution synthetic irradiance is of interest for theoretical studies such as grid integration of solar PV and battery storage analysis. Access to site-specific data is often limited to inadequate temporal resolutions for such application. A new model for producing synthetic solar global horizontal irradiance (GHI) time-series at up to 1-min resolution is presented as derived from >10-min input data. Briefly, it is a clustered-based method for daily clearness index distributions using Dirichlet process Gaussian mixture model (DPGMM). DPGMM is a nonparametric Bayesian (NPB) model indexed with an infinite-dimensional space of parameters. The key benefit of the NPB paradigm is the automatic adaptation to the correct complexity level and model size, suggesting a local adaptation of the model to all climatic conditions. A posterior inference using Markov chain Monte Carlo algorithm (namely Gibbs sampling) is applied. The model only requires a valid number of intraday data to construct daily distributions, then it can be applied worldwide. The synthetic GHI time series are validated against observed 1-min GHI data for four locations distributed throughout the world with different climatic conditions and significant geographic separation. Moreover, the presented method can generate data based on similar climatic conditions. A good fit between real and generated data is observed. We present an nRMSE ≤ 4% and nMBE < ±4% between generated and measured means at both daily and monthly scales for all sites. The agreement between the real and generated cumulative density distributions of six comparative variability metrics (defined in text) at four different sites is measured using the overlapping and the Kullback-Leibler coefficients, which are ≥ 75% and ≤ 10% respectively, in all cases. To ensure the reproducibility of the research presented in this paper, the methodology is freely available as an R-package downloadable from SolarClusGnr.

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“…As recognized by different authors and institutions (Abreu et al, 2018;Peruchena et al, 2018;Renné, 2016) the main source of uncertainty in large solar energy projects is the available solar resource. Ideally, the long-term assessment of the solar resource should be based on high quality time series of measured data recorded during a long period, at least 10 consecutive years.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Generation of Plausible Solar Years For Long-Term Forecasting of Solar Radiation

Jiménez-Valero¹,

Larrañeta

López-García³

et al. 2022

Preprint

Self Cite

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In this work we test and improve an algorithm proposed in previous studies to generate synthetic series of plausible solar years (PSY). The method provides 100 synthetic years of coupled global horizontal irradiance (GHI) and direct normal solar irradiance (DNI) in 1 min resolution. The algorithm uses 10–20 years of hourly coupled GHI + DNI datasets that can be retrieved for most of the locations of the world from satellite estimates. The algorithm is evaluated at five locations with different type of climate according to the Koppen-Geiger classification and at different temporal scales: annual, monthly, daily and 1-minute resolution. In all cases, synthetic PSYs series cover a wider range of scenarios than the observed series but maintaining their distribution. Results suggest that the synthetically generated PSYs are capable to reproduce the natural variability of the solar resource at any location facilitating the stochastic simulation of solar harnessing systems.

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High frequency generation of coupled GHI and DNI based on clustered Dynamic Paths

Cited by 19 publications

References 17 publications

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

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

Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data

Synthetic Generation of Plausible Solar Years For Long-Term Forecasting of Solar Radiation

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