Methodology to synthetically downscale DNI time series from 1-h to 1-min temporal resolution with geographic flexibility

Larrañeta, M. G.; Fernández-Peruchena, Carlos M.; Silva-Pérez, M.A.; Lillo-Bravo, I.

doi:10.1016/j.solener.2018.01.064

Cited by 18 publications

(5 citation statements)

References 24 publications

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“…In this step, we use the ND model tool (Larrañeta et al, 2019) for the synthetic generation of 1-min coupled GHI and DNI data. The ND model uses non-dimensional databases from a location with similar climate to the input data; therefore, it can be used in at location without local adaptation (M. Larrañeta et al, 2018). The non-dimensional database daily pro les are labeled with the indexes kt, kb, VI and Fm that provide information about energy, variability and distribution following Moreno-Tejera., 2017.).…”

Section: Methodsmentioning

confidence: 99%

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

Jiménez-Valero¹,

Larrañeta

López-García³

et al. 2022

Preprint

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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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Section: Methodsmentioning

confidence: 99%

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

Jiménez-Valero¹,

Larrañeta

López-García³

et al. 2022

Preprint

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“…In this step, we use the ND model tool [17] for the synthetic generation of 1-min coupled GHI and DNI data. The ND model uses non-dimensional databases from a location with similar climate to the input data; therefore, it can be used in at location without local adaptation [18]. The non-dimensional database daily profiles are labeled in terms of energy (kt, kb), variability (VI) and distribution (Fm).…”

Section: Previous Methodologymentioning

confidence: 99%

Synthetic generation of plausible solar years for long-term forecasting of solar radiation

Jiménez-Valero

Larrañeta

López-García

et al. 2022

Theor Appl Climatol

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En este trabajo se mejora y se valida un algoritmo propuesto en estudios previos para generar series sintéticas de años solares plausibles (PSY). La metodología proporciona 100 años sintéticos en paso minutal de radiación global horizontal (GHI) y radiación directa normal (DNI) acoplados. El algoritmo requiere un conjunto de datos de 10 a 20 años de GHI y DNI acoplados en paso horario que se pueden obtener para la mayoría de las ubicaciones del mundo a partir de estimaciones de satélite. El algoritmo se evalúa en cinco emplazamientos seleccionados por su diferente tipo de clima según la clasificación de Koppen-Geiger. La evaluación se realiza en diferentes escalas temporales: anual, mensual, diaria y minutal. En todos los casos, las series sintéticas de años solares plausibles cubren una gama más amplia de escenarios que las series observadas, pero manteniendo su distribución. Los resultados sugieren que los PSY generados sintéticamente son capaces de reproducir la variabilidad natural del recurso solar en cualquier emplazamiento facilitando la simulación estocástica de los sistemas de aprovechamiento solar.

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“…An alternative approach to obtaining high-resolution irradiance data is through synthetic irradiance generation. 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.…”

Section: An Overview Of Literaturementioning

confidence: 99%

“…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. Grantham et al (2017) developed a method for generating 5-minute resolution temporal-only synthetic time series of GHI and DNI from stored sequences guided by hourly average values.…”

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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Methodology to synthetically downscale DNI time series from 1-h to 1-min temporal resolution with geographic flexibility

Cited by 18 publications

References 24 publications

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

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

Synthetic generation of plausible solar years for long-term forecasting of solar radiation

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

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