Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems

Lorenz, Elke; Hurka, Johannes; Heinemann, Detlev; Beyer, Hans Georg

doi:10.1109/jstars.2009.2020300

Cited by 606 publications

(351 citation statements)

References 6 publications

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“…Improving forecasting of the available solar irradiance will support management of the electric grid and electricity markets and therefore ensure a more economical integration of solar power (Mathiesen et al, 2013). Currently several different methods are used to forecast at different spatial and temporal resolutions including numerical weather prediction (e.g., Lorenz et al, 2009;Mathiesen and Kleissl, 2011) and satellite imagebased forecasting (e.g., Hammer et al, 1999). For short-term forecasting, whole-sky imagery has been used (e.g., Urquhart et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

et al. 2016

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Abstract. A method for retrieving cloud optical depth (τ c ) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red-blue ratio (RRBR) method is motivated from the analysis of simulated images of various τ c produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red-blue ratio (RBR) of a pixel are identified as the solar zenith angle (θ 0 ), τ c , solar pixel angle/scattering angle (ϑ s ), and pixel zenith angle/view angle (ϑ z ). The effects of these parameters are described and the functions for radiance, I λ (τ c , θ 0 , ϑ s , ϑ z ), and RBR(τ c , θ 0 , ϑ s , ϑ z ) are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τ c , where RBR increases with τ c up to about τ c = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured I meas λ (ϑ s , ϑ z ), in addition to RBR meas (ϑ s , ϑ z ), to obtain a unique solution for τ c . The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003)

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Section: Introductionmentioning

confidence: 99%

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

et al. 2016

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“…These improvements have stemmed from including observations in the immediate vicinity of the resource, both in the nowcasting and assimilated into the NWP models, as well as better methods of blending multiple models for the appropriate timescales. Solar power predictions have not been a focus for very long, but rapid improvement is also happening here (Lorenz et al 2009;2014;Tuohy et al 2015;Haupt et al 2017).…”

Section: Probabilistic Forecasts and The Analog Ensemblementioning

confidence: 99%

Short-Range Forecasting for Energy

Haupt

2018

Weather &Amp; Climate Services for the Energy Industry

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Short-range forecasts for periods on the order of hours to days and up to two weeks ahead are necessary to smoothly run transmission and distribution systems, plan maintenance, protect infrastructure and allocate units. In particular, forecasting the renewable energy resources on a day-to-day basis enables integration of increasing capacities of these variable resources. This chapter describes the basics of this short-range forecasting, beginning with the observation-based "nowcasting" of the first 15 minutes and ranging up to two weeks using numerical weather prediction. We discuss how blending multiple forecasts can increase accuracy and how probabilistic forecasts are being used to quantify the forecast uncertainty.

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“…The main approaches, presented in the literature, to the problem of the PV short-term forecasting are summarized by Singh et al (2013), Kardakos et al (2013), Trapero et al (2014), Lorenz et al (2009), Marquez andCoimbra (2011), Bacher et al (2009). The authors of Singh et al (2013) propose an adaptive-neuro-fuzzy inference (ANFIS) to predict the PV power output in the time horizon of onehour ahead.…”

Section: State Of the Art: Prediction Intervalsmentioning

confidence: 99%

“…Concerning the prediction intervals, a common hypothesis adopted in the literature (e.g., Lorenz et al, 2009;Marquez and Coimbra, 2011) is to assume a normal distribution of the forecast errors. The magnitude of the prediction intervals are usually estimated as multiples of the standard deviation of the forecast solar-irradiance associated with a given confidence level.…”

Section: State Of the Art: Prediction Intervalsmentioning

confidence: 99%

“…The available literature on prediction intervals for PV energy-conversion systems is characterized by the following four main limitations (Singh et al, 2013;Kardakos et al, 2013;Trapero et al, 2014;Lorenz et al, 2009;Marquez and Coimbra, 2011;Bacher et al, 2009;Segura and Vercher, 2001): (i) absence of methods proposing prediction intervals targeting the time scale of seconds or sub-seconds; (ii) absence of methods proposing prediction intervals able to track the highly-dynamic volatility of the solar irradiance; (iii) absence of methods able to account for distributions of the point-forecast errors other than Gaussian; (iv) strong dependency of the prediction interval with the specific method used for the point forecast computation. To the best of our knowledge, the only works that are independent of the point-forecast method are (Wan et al, 2014;Pinson and Tastu, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Model-free computation of ultra-short-term prediction intervals of solar irradiance

2016

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We propose an ultra-short-term dynamic interval predictor (DIP) of solar irradiance. Our DIP relies on experimentally observed correlations between the derivative of the solar irradiance and the forecast error in the next time-step. The main originalities of this DIP are (i) its independence from the method used for the point forecast of solar irradiance, (ii) its independence from the error distribution of the point-forecast method. We compare the DIP with the most common prediction interval methods. By using significant data set covering several months of experimental observations, we have observed higher accuracy and lower width of the prediction intervals of the proposed DIP.

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Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems

Cited by 606 publications

References 6 publications

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

Short-Range Forecasting for Energy

Model-free computation of ultra-short-term prediction intervals of solar irradiance

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