Data compression technique for modeling of global solar radiation

Karim, Samsul Ariffin Abdul; Singh, Balbir Singh Mahinder; Razali, Radzuan; Yahya, Noorhana

doi:10.1109/iccsce.2011.6190550

Cited by 12 publications

(11 citation statements)

References 15 publications

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“…where f is a regression function and i  are zero-mean independent random error with a common variance 2  . The main idea in the regression analysis is to construct a mathematical model for f and estimate it based on the noisy data.…”

Section: Polynomial Data Fittingmentioning

confidence: 99%

“…Solar radiation prediction and forecasting are importance in the generation of electricity and to provide the alternative energy [1]. The demand for energy is increasing exponentially as the human population increases dynamically [2,3,4,5]. In Karim et al [2], wavelet transform has been used to compress the solar radiation data and to develop a new mathematical model for solar radiation data forecasting and prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the works of Wu and Chan [1] and Karim et al [2], in this paper the authors will use polynomial data fitting to predict the solar radiation data with good prediction accuracy at minimal error. The best solar radiation model is obtained by using quadratic polynomial fitting.…”

Section: Introductionmentioning

confidence: 99%

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Global solar radiation modeling using polynomial fitting

Karim¹,

Singh²

2014

ams

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This paper studies the use of polynomial fitting to model the global solar radiation that was measured by using solarimeter at Universiti Teknologi PETRONAS (UTP), located in Perak, Malaysia. The measured solar radiation data was filtered by using fitting and smoothing methods. The polynomial data fitting method was used for data smoothing and was tested by using different degrees of polynomial curve fittings. The error measurement was calculated by using the root mean square error (RMSE) and by determining the 2 R value. The polynomial fittings were carried out by using MATLAB.

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Section: Polynomial Data Fittingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global solar radiation modeling using polynomial fitting

Karim¹,

Singh²

2014

ams

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“…We analyzed random characteristics of solar radiation of previous literature [3,4,5] in the field of meteorology random, the literature [6][7][8][9][10] is from the randomness of solar radiation, and then study the randomness of the power. In previous studies [11,12], the influencing factors of photovoltaic power output characteristics from the viewpoint of power electronic devices were studied.…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis of Photovoltaic Power with the Field Data of Xinjiang Korla Photovoltaic Power Plants

Yang¹

2018

Proceedings of the 2018 3rd International Conference on Communications, Information Management and Network Security (CIMNS 2018

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Mastering the characteristics of photovoltaic power generation helps reduce the output limit of photovoltaic power generation and improve the absorption ability of photovoltaic power generation. Based on the measured data from photovoltaic power stations, the sunrise force-related characteristics of a photovoltaic power station are analyzed. Specifically, first, the correlation coefficient and the sunrise force characteristics of a photovoltaic power station are introduced. Second, the output data and time-space characteristics of two photovoltaic power stations are provided. Furthermore, based on the above actual data, the influence of weather and time on different time autocorrelation characteristics of a photovoltaic power station is analyzed. Based on the spatiotemporal characteristics of a photovoltaic power station, the correlation between time and space characteristics and photovoltaic power generation data of different substations is analyzed. Our study is conducive to further understanding the characteristics of photovoltaic power station data.

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“…Since wavelet transform has the capability to orthogonalize the data and produces the sparse matrix in the transformation, then wavelet transform give better results compared to the other image processing methods such as Gaussian, Fourier transform, Hilbert-Huang transform etc. Some applications of wavelet based methods are in finance, economy and solar radiations (Karim et al [1,2,3,4,5,6] and Al-Wadi et al [7]). …”

Section: Introductionmentioning

confidence: 99%