Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging

Yang, Di; Gu, Chenghong; Zhang, Dong; Jirutitijaroen, Panida; Chen, Nan; Walsh, Wilfred M.

doi:10.1016/j.renene.2013.05.030

Cited by 123 publications

(44 citation statements)

References 26 publications

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“…With increasing penetration levels of variable solar power into electricity grids, such irradiance maps as well as spatio-temporal solar irradiance forecasting techniques become more and more relevant. One such example where GHI data are required is spacetime kriging (Yang et al, 2013b. We introduce a novel technique in this section, namely, irradiance conversion from tilt to horizontal using two or more silicon sensors.…”

Section: Solutions To the Inverse Problemmentioning

confidence: 99%

Bidirectional irradiance transposition based on the Perez model

Yang

Ye²,

Nobre

et al. 2014

Solar Energy

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The Perez irradiance model offers a practical representation of solar irradiance by considering the sky hemisphere as a three part geometrical framework, namely, circumsolar disc, horizon band and the isotropic background. Furthermore, the simplified Perez diffuse irradiance model, commonly known as the Perez transposition model, is one of the most widely adopted models in tilted irradiance assessments. Although the set of model coefficients reported by Perez et al. (1990) is often considered to be at an asymptotic level of optimization, later analyses have shown that coefficients which are adjusted to local conditions perform better than the original set.The model coefficients can be adjusted locally based on multiple datasets of diffuse and global irradiance on tilted and horizontal planes. In this paper, we present a different approach to adjust the coefficients, by using only measurements of global irradiance on tilted and horizontal planes from a tropical site, Singapore. A complete set of mathematical solutions to the inverse problem, i.e., converting irradiance from tilt to horizontal is also proposed. The data can then be used to generate irradiance maps from in-plane irradiance measurements at photovoltaic (PV) systems. Such maps are get- * Corresponding author. ting more relevant for PV grid integration due to the variable nature of solar power output. Keywords: Perez model, reference cells, tropical regions, inverse transpositionList of symbols.

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Section: Solutions To the Inverse Problemmentioning

confidence: 99%

Bidirectional irradiance transposition based on the Perez model

Yang

Ye²,

Nobre

et al. 2014

Solar Energy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Most applications of solar [5][6][7][8][9][10][11][12][13][14][15][16] and wind [17][18][19] forecasting have been at these shorter durations, developing a wide variety of methods and tools. However, there is also longer-term variability in solar and wind resources, beyond the mean annual cycle.…”

Section: Introductionmentioning

confidence: 99%

Interannual Variability and Seasonal Predictability of Wind and Solar Resources

Krakauer

Cohan

2017

Resources

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Solar and wind resources available for power generation are subject to variability due to meteorological factors. Here, we use a new global climate reanalysis product, Version 2 of the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA-2), to quantify interannual variability of monthly-mean solar and wind resource from 1980 to 2016 at a resolution of about 0.5 degrees. We find an average coefficient of variation (CV) of 11% for monthly-mean solar radiation and 8% for wind speed. Mean CVs were about 25% greater over ocean than over land and, for land areas, were greatest at high latitude. The correlation between solar and wind anomalies was near zero in the global mean, but markedly positive or negative in some regions. Both wind and solar variability were correlated with values of climate modes such as the Southern Oscillation Index and Arctic Oscillation, with correlations in the Northern Hemisphere generally stronger during winter. We conclude that reanalysis solar and wind fields could be helpful in assessing variability in power generation due to interannual fluctuations in the solar and wind resource. Skillful prediction of these fluctuations seems to be possible, particularly for certain regions and seasons, given the persistence or predictability of climate modes with which these fluctuations are associated.

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“…Although exponential smoothing has the multivariate form [19], to realize the vector exponential smoothing, a network of irradiance monitoring stations are required to sample the time series of lattice process [20]. In addition, the selection of relevant spatial neighbors needs attention, as irrelevant information not only increases the model complexity but also introduce additional errors [21].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of global horizontal irradiance by exponential smoothing, using decompositions

Yang

Sharma

Ye³

et al. 2015

Energy

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116

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Time series methods are frequently used in solar irradiance forecasting when two dimensional cloud information provided by satellite or sky camera is unavailable. Furthermore, satellite and sky camera based methods lose resolution at a 1-h time horizon. Exponential smoothing (ETS) has received great attention in the recent years due to the invention of its state space formulation. We explore 1-h ahead ETS forecasting of solar irradiance in this paper. Several knowledge based decompositions are considered to improve the forecast accuracy and computation speed.Three methods are proposed. The first method considers an additive seasonal-trend decomposition prior to the use of ETS. In such a way, the ETS state space is reduced, thus facilitates online forecasting applications. The second method decomposes the global horizontal irradiance (GHI) time series into a direct component and a diffuse component. These two components are used as forecasting model inputs separately; the results are recombined through the closure equation. We consider the time series of cloud cover index in the third method. ETS is first applied to the cloud cover time series to produce the forecast. The forecast cloud cover is then used to reconstruct GHI through regressions. It is found that the third method performs the best among three methods and all proposed methods outperforms persistence.

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Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging

Cited by 123 publications

References 26 publications

Bidirectional irradiance transposition based on the Perez model

Bidirectional irradiance transposition based on the Perez model

Interannual Variability and Seasonal Predictability of Wind and Solar Resources

Forecasting of global horizontal irradiance by exponential smoothing, using decompositions

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