Probabilistic wind power forecasting based on logarithmic transformation and boundary kernel

Zhang, Yao; Wang, Jianxue; Xu, Luo

doi:10.1016/j.enconman.2015.03.012

Cited by 53 publications

(24 citation statements)

References 38 publications

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“…As indicated by (8), the weight ω decays as d increases, and the parameter η d controls the slope of the decay.…”

Section: A Conditional Probabilistic Forecastingmentioning

confidence: 99%

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Forecasted Scenarios of Regional Wind Farms Based on Regular Vine Copulas

Wang

et al. 2020

Journal of Modern Power Systems and Clean Energy

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Owing to the uncertainty and volatility of wind energy, forecasted wind power scenarios with proper spatio-temporal correlations are needed in various decision-making problems involving power systems. In this study, forecasted scenarios are generated from an estimated multi-variate distribution of multiple regional wind farms. According to the theory of copulas, marginal distributions and the dependence structure of multi-variate distribution are modeled through the proposed distance-weighted kernel density estimation method and the regular vine (R-vine) copula, respectively. Owing to the flexibility of decomposing correlations of high dimensions into different types of pair-copulas, the R-vine copula provides more accurate results in describing the complicated dependence of wind power. In the case of 26 wind farms located in East China, highquality forecasted scenarios as well as the corresponding probabilistic forecasting and point forecasting results are obtained using the proposed method, and the results are evaluated using a comprehensive verification framework.

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“…As indicated by (8), the weight ω decays as d increases, and the parameter η d controls the slope of the decay.…”

Section: A Conditional Probabilistic Forecastingmentioning

confidence: 99%

“…Prediction interval estimation methods employing machinelearning algorithms are introduced [5], [6]. For providing a continuous probability density function (PDF), kernel density estimation (KDE) methods are presented [7], [8]. A method based on meteorological ensembles of numerical weather prediction (NWP) systems is discussed in [9].…”

Section: Introductionmentioning

confidence: 99%

Forecasted Scenarios of Regional Wind Farms Based on Regular Vine Copulas

Wang

et al. 2020

Journal of Modern Power Systems and Clean Energy

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“…These methods can be divided into two main groups, which include deterministic [23,24] and probabilistic approaches [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

“…[26] presents a non-parametric approach to model the actual wind power curve of a WF. Kernel density estimation (KDE) method has been used in [27] to probabilistic estimation of WF's power output. Also, [28] proposes a method based on wavelet neural network (WNN) to forecast wind power, which uses Morlet wavelets as activation functions in the hidden layer.…”

Section: Introductionmentioning

confidence: 99%

A new method to adequate assessment of wind farms’ power output

Zolfaghari

Riahy

Abedi

2015

Energy Conversion and Management

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“…A general framework of probabilistic forecasting for renewable energy generation, especially for wind power generation, has been presented in previous articles [43] and [44]. It is a combination of k-nearest neighbors (k-NN) algorithm and kernel density estimator (KDE) method.…”

Section: A Scenario Generationmentioning

confidence: 99%

Chance-Constrained Two-Stage Unit Commitment Under Uncertain Load and Wind Power Output Using Bilinear Benders Decomposition

Zhang

Wang

Zeng

et al. 2017

IEEE Trans. Power Syst.

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118

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Abstract-In this paper, we study unit commitment (UC) problems considering the uncertainty of load and wind power generation. UC problem is formulated as a chance-constrained two-stage stochastic programming problem where the chance constraint is used to restrict the probability of load imbalance. In addition to the conventional mixed integer linear programming formulation using Big-M, we present the bilinear mixed integer formulation of chance constraint, and then derive its linear counterpart using McCormick linearization method. Then, we develop a bilinear variant of Benders decomposition method, which is an easy-to-implement algorithm, to solve the resulting large-scale linear counterpart. Our results on typical IEEE systems demonstrate that (i) the bilinear mixed integer programming formulation is stronger than the conventional one; (ii) the proposed Benders decomposition algorithm is generally an order of magnitude faster than using a professional solver to directly compute both linear and bilinear chance-constrained UC models.

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Probabilistic wind power forecasting based on logarithmic transformation and boundary kernel

Cited by 53 publications

References 38 publications

Forecasted Scenarios of Regional Wind Farms Based on Regular Vine Copulas

Forecasted Scenarios of Regional Wind Farms Based on Regular Vine Copulas

A new method to adequate assessment of wind farms’ power output

Chance-Constrained Two-Stage Unit Commitment Under Uncertain Load and Wind Power Output Using Bilinear Benders Decomposition

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