Small-scale solar radiation forecasting using ARMA and nonlinear autoregressive neural network models

Benmouiza, Khalil; Cheknane, Ali

doi:10.1007/s00704-015-1469-z

Cited by 102 publications

(40 citation statements)

References 30 publications

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“…While ARMA model developed with Expectation Maximisation algorithm is robust and reliable in short-term forecasting for heterogeneous dataset [211], David et al [212] improved the prediction reliability of recursive ARMA model through its association with GARCH model, wherein uncertainties in solar forecasting were captured. Besides, Benmouiza and Cheknane [213] hybridised ARMA and NAR models for simultaneous capturing of linear and non-linear solar irradiance time series patterns.…”

Section: Referencesmentioning

confidence: 99%

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo

Lim

et al. 2017

Renewable and Sustainable Energy Reviews

232

105

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Section: Referencesmentioning

confidence: 99%

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo

Lim

et al. 2017

Renewable and Sustainable Energy Reviews

232

105

View full text Add to dashboard Cite

“…Otherwise, the null hypothesis should be accepted and the time series is nonstationary. Different detrending models are addressed and compared in [26] and [27].…”

Section: A Data Prepration and Preprocessingmentioning

confidence: 99%

Two-stage hybrid day-ahead solar forecasting

Alanazi

Mahoor

Khodaei

2017

2017 North American Power Symposium (NAPS)

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Abstract-Power supply from renewable resources is on a global rise where it is forecasted that renewable generation will surpass other types of generation in a foreseeable future. Increased generation from renewable resources, mainly solar and wind, exposes the power grid to more vulnerabilities, conceivably due to their variable generation, thus highlighting the importance of accurate forecasting methods. This paper proposes a two-stage day-ahead solar forecasting method that breaks down the forecasting into linear and nonlinear parts, determines subsequent forecasts, and accordingly, improves accuracy of the obtained results. To further reduce the error resulted from nonstationarity of the historical solar radiation data, a data processing approach, including pre-process and post-process levels, is integrated with the proposed method. Numerical simulations on three test days with different weather conditions exhibit the effectiveness of the proposed two-stage model.

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“…Moreover, after some trial-error process, we see that keeping the interval narrower (smaller δ values) increases the number of acceptable moves. That's why we have chosen a value of 0.02 for the nominator part in equation (8).…”

Section: Rjmcmc Construction For Parma Model Estimationmentioning

confidence: 99%

“…ARMA models have been used in diverse areas of applications such as speech [2], [3], seismology [4], video [5], image [6], etc. Particularly, they have been applied in energy and meteorological prediction studies of solar radiation [7], [8], electricity demand [9], [10] and wind speed [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear model selection for PARMA processes using RJMCMC

Karakuş

Kuruoğlu

Altınkaya

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Many prediction studies using real life measurements such as wind speed, power, electricity load and rainfall utilize linear autoregressive moving average (ARMA) based models due to their simplicity and general character. However, most of the real life applications exhibit nonlinear character and modelling them with linear time series may become problematic. Among nonlinear ARMA models, polynomial ARMA (PARMA) models belong to the class of linear-in-the-parameters. In this paper, we propose a reversible jump Markov chain Monte Carlo (RJMCMC) based complete model estimation method which estimates PARMA models with all their parameters including the nonlinearity degree. The proposed method is unique in the manner of estimating the nonlinearity degree and all other model orders and model coefficients at the same time. Moreover, in this paper, RJMCMC has been examined in an anomalous way by performing transitions between linear and nonlinear model spaces.

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Small-scale solar radiation forecasting using ARMA and nonlinear autoregressive neural network models

Cited by 102 publications

References 30 publications

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Two-stage hybrid day-ahead solar forecasting

Nonlinear model selection for PARMA processes using RJMCMC

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