Statistical distribution of the clearness index with radiation data integrated over five minute intervals

Jurado, M.; Caridad, J.M.; Ruíz, Valeriano

doi:10.1016/0038-092x(95)00067-2

Cited by 85 publications

(27 citation statements)

References 6 publications

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“…-k j,m as in Equation (1), -ε j,m pn∆tq given by: ε j,m pn∆tq " S LJ j,m pn∆tq¨rε [19] of solar irradiance, R j,m pn∆tq, of its components k j,m¨SW j,m pn∆tq and ε j,m pn∆tq and of the expected irradiance at ground level, S W j,m pn∆tq according to Equation (6). Of course, a similar interpretation of the same figure can be effected in terms of the components given by Equation (1).…”

Section: Proposed Modelmentioning

confidence: 99%

“…Papers [3][4][5][6][7][8][9][10][11][12][13] refer to the clearness index and emphasize that the probability density distributions of the clearness index on short-term intervals, unlike those considered in longer intervals, present a multimodal nature [3,12]. In [13] it is stated that the instantaneous solar irradiance (in terms of clearness index) has bimodal probability distributions with peaks corresponding to clear and cloudy conditions.…”

Section: Introductionmentioning

confidence: 99%

“…This has been treated in the relevant technical literature in [3][4][5][6][7][8][9][10][11][12][13][14][15][16], where the clear sky index and the clearness index (or the normalized clearness index [12]) are typically used to describe solar irradiance and radiation.…”

Section: Introductionmentioning

confidence: 99%

“…In [13] it is stated that the instantaneous solar irradiance (in terms of clearness index) has bimodal probability distributions with peaks corresponding to clear and cloudy conditions. Regarding the statistical characterization of solar quantities, several attempts were proposed to identify the distribution functions that best fit solar data on a short time basis, some based on the Boltzmann statistics ( [3,4]), others referring to the mixture of two normal distributions ( [5,6]) or two bi-exponential probability density functions ( [7,8]). These aspects make such methods mainly suitable for very short time forecasting with no exogenous inputs, as shown in [2], where five different techniques were assessed for one hour ahead and two hours ahead power forecasting of PV plants.…”

Section: Introductionmentioning

confidence: 99%

“…Common operational approaches to solar radiation forecasting include: (1) numerical weather prediction (NWP) models that infer local cloud information through the dynamic modeling of the atmosphere up to several days ahead [1]; (2) models using satellite remote sensing or ground-based sky measurements to infer the motion of clouds and project their impact in the future; (3) statistical time series models based on measured irradiance data applied for very short term forecasting in the range of minutes to hours [2][3][4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Solar Radiation Forecasting, Accounting for Daily Variability

2016

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Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical expression but is not bound by a specific clear sky model; it accounts separately for the mean daily variability and for the variation of solar irradiance during the day by means of two corrective parameters. This new proposal allows for a better understanding of the physical phenomena and improves the effectiveness of statistical characterization and subsequent simulation of the introduced parameters to generate a synthetic solar irradiance time series. Furthermore, the analysis of the experimental distributions of the two parameters' data was developed, obtaining opportune fittings by means of parametric analytical distributions or mixtures of more than one distribution. Finally, the model was further improved toward the inclusion of weather prediction information in the solar irradiance forecasting stage, from the perspective of overcoming the limitations of purely statistical approaches and implementing a new tool in the frame of solar irradiance prediction accounting for weather predictions over different time horizons.

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Section: Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solar Radiation Forecasting, Accounting for Daily Variability

2016

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Probabilistic analysis of weather data for a hybrid solar/wind energy system

Tina

Gagliano

2011

Int. J. Energy Res.

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SUMMARYIn this paper, a procedure for the probabilistic treatment of solar irradiance and wind speed data is reported as a method of evaluating, at a given site, the electric energy generated by both a photovoltaic system and a wind system. The aim of the proposed approach is twofold: first, to check if the real probability distribution functions (PDFs) of both clearness index and wind speed overlap with Hollands and Huget and Weibull PDFs, respectively; and then to find the parameters of these two distributions that best fit the real data. Further, using goodness-of-fit tests, these PDFs are compared with another set of very common PDFs, namely the Gordon and Reddy and Lognormal functions, respectively.The results inform the design of a pre-processing stage for the input of an algorithm that probabilistically optimizes the design of hybrid solar wind power systems. In this paper, the validity of the proposed procedure was tested using long-term meteorological data from Acireale (Italy).

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Empirical assessment of short‐term variability from utility‐scale solar PV plants

Haaren

Morjaria

Fthenakis

2012

Progress in Photovoltaics

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Variability of solar power is a key driver in increasing the cost of integrating solar power into the electric grid because additional system resources are required to maintain the grid's reliability. In this study, we characterize the variability in power output of six photovoltaic plants in the USA and Canada with a total installed capacity of 195 MW (AC); it is based on minute‐averaged data from each plant and the output from 390 inverters. We use a simple metric, “daily aggregate ramp rate” to quantify, categorize, and compare daily variability across these multiple sites. With this metric, the effect of geographic dispersion is observed, while controlling for climatic differences across the plants. Additionally, we characterized variability due to geographical dispersion by simulating a step by step increase of the plant size at the same location. We observed maximum ramp rates for 5, 21, 48, and 80 MWAC plants, respectively, as 0.7, 0.58, 0.53, and 0.43 times the plant's capacity. Copyright © 2012 John Wiley & Sons, Ltd.

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Statistical distribution of the clearness index with radiation data integrated over five minute intervals

Cited by 85 publications

References 6 publications

Solar Radiation Forecasting, Accounting for Daily Variability

Solar Radiation Forecasting, Accounting for Daily Variability

Probabilistic analysis of weather data for a hybrid solar/wind energy system

Empirical assessment of short‐term variability from utility‐scale solar PV plants

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