A multiobjective framework for wind speed prediction interval forecasts

Shrivastava, Nitin Anand; Lohia, Kunal; Panigrahi, Bijaya Ketan

doi:10.1016/j.renene.2015.08.038

Cited by 74 publications

(36 citation statements)

References 24 publications

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“…Other approaches regarding the prediction intervals of renewable resources, the price of energy, and the electricity demand have been reported ( Hu, Hu, Yue, Zhang, & Hu, 2017;Li et al, 2018;Shrivastava et al, 2015Shrivastava et al, , 2016Voyant et al, 2018 ). In the works of Shrivastava et al (2016) and Shrivastava et al (2015) , methodologies were proposed based on the support vector machine (SVM) to generate the prediction intervals for wind speed and electricity costs. In Shrivastava et al (2016) , a multi-objective differential evolution algorithm was used to tune model parameters such that multiple opposing objectives were achieved to generate Paretooptimal solutions.…”

Section: Literature Review For Prediction Interval Modellingmentioning

confidence: 99%

“…Although computational intelligence methods exhibit adequate performance in estimation and prediction, uncertainty is not typically quantified by these modelling approaches, and only expected value is obtained. However, information on the dispersion of the output of the model provides more information about the phenomena modelled with uncertainty and more useful information from a decision-making point of view than the models with only expected value ( Kabir, Khosravi, Hosen, & Nahavandi, 2018;Shrivastava, Lohia, & Panigrahi, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks

Marín

Cruz

Sáez

et al. 2019

Expert Systems with Applications

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Prediction interval modelling has been proposed in the literature to characterize uncertain phenomena and provide useful information from a decision-making point of view. In most of the reported studies, assumptions about the data distribution are made and/or the models are trained at one step ahead, which can decrease the quality of the interval in terms of the information about the uncertainty modelled for a higher prediction horizon. In this paper, a new prediction interval modelling methodology based on fuzzy numbers is proposed to solve the abovementioned drawbacks. Fuzzy and neural network prediction interval models are developed based on this proposed methodology by minimizing a novel criterion that includes the coverage probability and normalized average width. The fuzzy number concept is considered because the affine combination of fuzzy numbers generates, by definition, prediction intervals that can handle uncertainty without requiring assumptions about the data distribution. The developed models are compared with a covariance-based prediction interval method, and high-quality intervals are obtained, as determined by the narrower interval width of the proposed method. Additionally, the proposed prediction intervals are tested by forecasting up to two days ahead of the load of the Huatacondo microgrid in the north of Chile and the consumption of the residential dwellings in the town of Loughborough, UK. The results show that the proposed models are suitable alternatives to electrical consumption forecasting because they obtain the minimum interval widths that characterize the uncertainty of this type of stochastic process. Furthermore, the information provided by the obtained prediction interval could be used to develop robust energy management systems that, for example, consider the worst-case scenario.

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Section: Literature Review For Prediction Interval Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks

Marín

Cruz

Sáez

et al. 2019

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…In order to cater the uncertainties in SVM-based wind speed forecasting, Shrivastava et al [245] proposed a multi-objective differential evolution (MODE) algorithm for quantification of chaotic nature of wind speed data and generating Pareto-optimal prediction. In another study, Meng et al [246] employed crisscross optimisation algorithm for developing a hybrid WNN wind forecasting model, with avoidance of premature convergence problem as in the case of Particle Swarm Optimisation (PSO) and backpropagation algorithm.…”

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

243

105

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“…Compared with other intelligent methods, it has the advantages of having a simple structure, fast calculation speeds, a high forecasting accuracy, and fewer training sample requirements [15][16][17][18]. According to the wind speed forcasting process, the ELM for regression is constructed in this paper using multiple inputs and a single output.…”

Section: Weighted Regularization Extreme Learning Machine (Wrelm)mentioning

confidence: 99%

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

et al. 2016

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Accurate wind speed forecasting is a fundamental element of wind power prediction. Thus, a new hybrid wind speed forecasting model, using variational mode decomposition (VMD), the partial autocorrelation function (PACF), and weighted regularized extreme learning machine (WRELM), is proposed to improve the accuracy of wind speed forecasting. First, the historic wind speed time series is decomposed into several intrinsic mode functions (IMFs). Second, the partial correlation of each IMF sequence is analyzed using PACF to select the optimal subfeature set for particular predictors of each IMF. Then, the predictors of each IMF are constructed in order to enhance its strength using WRELM. Finally, wind speed is obtained by adding up all the predictors. The experiment, using real wind speed data, verified the effectiveness and advancement of the new approach.

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A multiobjective framework for wind speed prediction interval forecasts

Cited by 74 publications

References 24 publications

Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks

Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks

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

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

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