Hourly solar irradiance time series forecasting using cloud cover index

Yang, Di; Jirutitijaroen, Panida; Walsh, Wilfred M.

doi:10.1016/j.solener.2012.07.029

Cited by 199 publications

(47 citation statements)

References 18 publications

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“…Such methods may consist of using techniques such as artificial neural networks (ANN-Mellit 2008;Wang et al 2012), autoregressive models (Hassanzadeh et al 2010;Yang et al 2012), Markov process models (Morf 2014) or support vector machines (Sharma et al 2011;Bouzerdoum et al 2013) to recognize patterns in the changes of wind speed or solar radiation. Blended techniques, such as that of Pedro and Coimbra (2012), that use a genetic algorithm to optimize an ANN have also been effective.…”

Section: Nowcastingmentioning

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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Section: Nowcastingmentioning

confidence: 99%

Short-Range Forecasting for Energy

Haupt

2018

Weather &Amp; Climate Services for the Energy Industry

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“…It was shown that the AR with observed solar power and the forecasted solar irradiance performs better than the AR model for solar power alone. Furthermore, an autoregressive integrated moving average (ARIMA) model was designed to forecast the GHI [8]. The ARIMA model predicted a cloud index and then forecasted the GHI through a look up table of a cloud index based on a zenith angle and the predicted cloud index.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial and Temporal Day-Ahead Total Daily Solar Irradiation Forecasting: Ensemble Forecasting Based on the Empirical Biasing

Baek

Lee

2017

Energies

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Total daily solar irradiation for the next day is forecasted through an ensemble of multiple machine learning algorithms using forecasted weather scenarios from numerical weather prediction (NWP) models. The weather scenarios were predicted at grid points whose longitudes and latitudes are integers, but the total daily solar irradiation was measured at non-integer grid points. Therefore, six interpolation functions are used to interpolate weather scenarios at non-integer grid points, and their performances are compared. Furthermore, when the total daily solar irradiation for the next day is forecasted, many data trimming techniques, such as outlier detection, input data clustering, input data pre-processing, and output data post-processing techniques, are developed and compared. Finally, various combinations of these ensemble techniques, different NWP scenarios, and machine learning algorithms are compared. The best model is to combine multiple forecasting machines through weighted averaging and to use all NWP scenarios.

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“…Robaa (2008) has done the evaluation of sunshine duration from cloud data in Egypt. Yang et al (2012) have done the hourly solar irradiance time series forecasting using cloud cover index. Nimnuan and Janjai (2012) have found another approach for estimating average daily global solar radiation from cloud cover in Thailand.…”

Section: Introductionmentioning

confidence: 99%

Global solar radiation estimation from commonly available meteorological data for Bangladesh

Sarkar

Sifat

2016

Renewables

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In this study, several regression models were employed to estimate global solar radiation from commonly available meteorological data such as sunshine duration, temperature, precipitation, and cloud cover for 34 meteorological stations of Bangladesh. The models studied were calibrated using five meteorological stations that are providing global solar radiation as well as other meteorological data. Estimated values were also compared with measured values in terms of statistical evaluation indicators like the coefficient of determination (R 2 ), mean percentage error, mean bias error, root mean square error (RMSE), mean absolute relative error, and t statistic. The statistical analysis showed that the models assessed were well suited to accurately estimate the solar potential. Sunshine duration-based models performed best, and cloud cover-based models performed worst. Among 45 developed models to predict solar radiation, the models with RMSE value lower than 0.2 are recommended for use.

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Hourly solar irradiance time series forecasting using cloud cover index

Cited by 199 publications

References 18 publications

Short-Range Forecasting for Energy

Short-Range Forecasting for Energy

Spatial and Temporal Day-Ahead Total Daily Solar Irradiation Forecasting: Ensemble Forecasting Based on the Empirical Biasing

Global solar radiation estimation from commonly available meteorological data for Bangladesh

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