Estimation of solar radiation using a combination of Hidden Markov Model and generalized Fuzzy model

Bhardwaj, Saurabh; Sharma, Vikrant; Srivastava, Smriti; Sastry, O.S.; Bandyopadhyay, Bidhan C.; Chandel, S.S.; Gupta, J. R. P.

doi:10.1016/j.solener.2013.03.020

Cited by 124 publications

(44 citation statements)

References 25 publications

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“…Markov models are a very popular method of stochastic data generation and have been used in many applications, from wind estimates (Masseran, 2015), solar energy estimations (Bhardwaj et al, 2013;Vindel and Polo, 2014b;Hocaoglu, 2011), and in weather generation (Yang et al, 2011).…”

Section: Markov Chainsmentioning

confidence: 99%

Stochastic generation of synthetic minutely irradiance time series derived from mean hourly weather observation data

et al. 2015

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Synthetic minutely irradiance time series are utilised in non-spatial solar energy system research simulations. It is necessary that they accurately capture irradiance fluctuations and variability inherent in the solar resource. This article describes a methodology to generate a synthetic minutely irradiance time series from widely available hourly weather observation data. The weather observation data are used to produce a set of Markov chains taking into account seasonal, diurnal, and pressure influences on transition probabilities of cloud cover. Cloud dynamics are based on a power-law probability distribution, from which cloud length and duration are derived. Atmospheric transmission losses are simulated with minutely variability, using atmospheric profiles from meteorological reanalysis data and cloud attenuation derived real-world observations. Both direct and diffuse irradiance are calculated, from which total irradiance is determined on an arbitrary plane. The method is applied to the city of Leeds, UK, and validated using independent hourly radiation measurements from the same site. Variability and ramp rate are validated using 1-min resolution irradiance data from the town of Cambourne, Cornwall, UK. The hourly irradiance frequency distribution correlates with R 2 ¼ 0:996 whilst the mean hourly irradiance correlates with R 2 ¼ 0:971, the daily variability indices cumulative probability distribution function (CDF), 1-min irradiance ramp rate CDF and 1-min irradiance frequency CDF are also shown to correlate with R 2 ¼ 0:9903; 1:000, and 0:9994 respectively. KolmogorovSmirnov tests on 1-min data for each day show that the ramp rate frequency of occurrence is captured with a high significance level of 99.99%, whilst the irradiance frequency distribution and minutely variability indices are captured at significances of 99% and 97.5% respectively. The use of multiple Markov chains and detailed consideration of the atmospheric losses are shown to be essential elements for the generation of realistic minutely irradiance time series over a typical meteorological year. A freely downloadable example of the model is made available and may be configured to the particular requirements of users or incorporated into other models.

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Section: Markov Chainsmentioning

confidence: 99%

Stochastic generation of synthetic minutely irradiance time series derived from mean hourly weather observation data

et al. 2015

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“…By comparing the prediction performance of the proposed technique with some other algorithms, they found higher accuracy and consistency for their approach. Bhardwaj et al (2013) proposed a hybrid approach which includes hidden Markov models and generalized fuzzy models to estimate solar irradiation in India. They assessed the influence of different meteorological parameters for estimation of solar radiation using the developed model.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

Shamshirband

Mohammadi

Chong

et al. 2015

Theor Appl Climatol

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In this study, a hybrid support vector machine-firefly optimization algorithm (SVM-FFA) model is proposed to estimate monthly mean horizontal global solar radiation (HGSR). The merit of SVM-FFA is assessed statistically by comparing its performance with three previously used approaches. Using each approach and long-term measured HGSR, three models are calibrated by considering different sets of meteorological parameters measured for Bandar Abbass situated in Iran. It is found that the model (3) utilizing the combination of relative sunshine duration, difference between maximum and minimum temperatures, relative humidity, water vapor pressure, average temperature, and extraterrestrial solar radiation shows superior performance based upon all approaches. Moreover, the extraterrestrial radiation is introduced as a significant parameter to accurately estimate the global solar radiation. The survey results reveal that the developed SVM-FFA approach is greatly capable to provide favorable predictions with significantly higher precision than other examined techniques. For the SVM-FFA (3), the statistical indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and coefficient of determination (R 2 ) are 3.3252 %, 0.1859 kWh/m 2 , 3.7350 %, and 0.9737, respectively which according to the RRMSE has an excellent performance. As a more evaluation of SVM-FFA (3), the ratio of estimated to measured values is computed and found that 47 out of 48 months considered as testing data fall between 0.90 and 1.10. Also, by performing a further verification, it is concluded that SVM-FFA (3) offers absolute superiority over the empirical models using relatively similar input parameters. In a nutshell, the hybrid SVM-FFA approach would be considered highly efficient to estimate the HGSR.

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“…In Ref. [34] solar irradiation in India is analyzed using a hybrid approach that combines Hidden Markov Models and generalized fuzzy models. According to Diagne et al [35], forecasting of global horizontal irradiance (GHI) can be categorized according to the input variables used, that also determine the forecast horizon where they perform best.…”

Section: Introductionmentioning

confidence: 99%

A CRO-species optimization scheme for robust global solar radiation statistical downscaling

et al. 2017

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a b s t r a c tThis paper tackles the prediction of the global solar radiation (GSR) at a given point, using as predictive variables the outputs of a numerical weather model (the WRF meso-scale model) obtained at a different grid points. Prediction is obtained in this work using a Multilayer Perceptron (MLP) trained with Extreme Learning Machines (ELMs). Provided that the number of WRF outputs is vast, we propose the use of a Coral Reefs Optimization algorithm with species (CRO-SP) to obtain a reduced number of significant predictive variables, therefore improving the global solar radiation prediction attained without feature selection. The proposed system has been tested on real data from a radiometric station located at Toledo (Spain) and average best results of RMSE of 69.19 W=m 2 have been achieved, resulting in a 21.62% improvement over the average prediction without considering the CRO-SP for the feature selection.

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Estimation of solar radiation using a combination of Hidden Markov Model and generalized Fuzzy model

Cited by 124 publications

References 25 publications

Stochastic generation of synthetic minutely irradiance time series derived from mean hourly weather observation data

Stochastic generation of synthetic minutely irradiance time series derived from mean hourly weather observation data

RETRACTED ARTICLE: A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

A CRO-species optimization scheme for robust global solar radiation statistical downscaling

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