Development of statistical time series models for solar power prediction

Prema, V.; Rao, K. Uma

doi:10.1016/j.renene.2015.03.038

Cited by 119 publications

(41 citation statements)

References 3 publications

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“…The best results shown are with a combination of SVR and Markov Chain models. Prema and Rao [6] tried different statistical models by taking time series data of solar radiation and predicted one day ahead. They found application of Moving Average (MA) method to produce better results by taking time series data.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2017

IJSC

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With present stress, being laid on green energy worldwide, harnessing solar energy for commercial use has importance in sizing and longterm prediction of solar radiation. However, with continuous changing environment parameters, it is quite difficult for long-term prediction of solar radiation. In the past research scholars, have carried out solar prediction only for a few days, which is insufficient to exploit the radiation for sizing and harnessing the solar energy for commercial use. To overcome this gap, present work utilizes application of lifting wavelet transform along with ANFIS to predict the radiation for long duration.

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Section: Related Workmentioning

confidence: 99%

Untitled

2017

IJSC

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“…The physical method which is combining with the geographical factors is the process of analysis and calculation on the basis of improving the resolution of the numerical weather forecast (NWP). Statistical method is establishing a mapping relationship between input and output based on a large number of historical data, and the commonly used methods include grey prediction [4][5][6], time series prediction model [7][8][9] and so on. The artificial intelligence technology describes the nonlinear relationship between system's inputs and outputs by means of artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

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2018

Adv. Electr. Comp. Eng.

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“…Numerical weather forecast is difficult to obtain, it needs to be supported by the abundant data in the field of statistics or meteorology. Statistical model [2][3][4] is a mapping relationship between the input of the system and the power of wind power generation. The commonly used methods mainly include Autoregressive moving average model (ARMA), generalized ARCH model (GARCH), grey prediction, empirical mode decomposition, Kalman filter model, etc.…”

Section: Introductionmentioning

confidence: 99%

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2017

Adv. Electr. Comp. Eng.

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1 Abstract-As conventional energy sources are nonrenewable, the world's major countries are investing heavily in renewable energy research. Wind power represents the development trend of future energy, but the intermittent and volatility of wind energy are the main reasons that leads to the poor accuracy of wind power prediction. However, by analyzing the error level at different time points, it can be found that the errors of adjacent time are often approximately the same, the least square support vector machine (LS-SVM) model with error correction is used to predict the wind power in this paper. According to the simulation of wind power data of two wind farms, the proposed method can effectively improve the prediction accuracy of wind power, and the error distribution is concentrated almost without deviation. The improved method proposed in this paper takes into account the error correction process of the model, which improved the prediction accuracy of the traditional model (RBF, Elman, LS-SVM). Compared with the single LS-SVM prediction model in this paper, the mean absolute error of the proposed method had decreased by 52 percent. The research work in this paper will be helpful to the reasonable arrangement of dispatching operation plan, the normal operation of the wind farm and the large-scale development as well as fully utilization of renewable energy resources.

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Development of statistical time series models for solar power prediction

Cited by 119 publications

References 3 publications

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