Adaptive Learning in Time-Variant Processes With Application to Wind Power Systems

Byon, Eunshin; Choe, Youngjun; Yampikulsakul, Nattavut

doi:10.1109/tase.2015.2440093

Cited by 35 publications

(17 citation statements)

References 23 publications

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“…In modeling F (t, S(t)), any type of functions, e.g., parametric, semi-parametric such as splines [25], or nonparametric function [26], [27], can be employed as long as F (t, S(t)) satisfies some weak conditions. Suppose that the power curve function F (t, S(t)) is differentiable over t and S(t) and twice differentiable over S(t).…”

Section: B Modeling Wind-to-power Conversion Processmentioning

confidence: 99%

Integrative Density Forecast and Uncertainty Quantification of Wind Power Generation

Wang

AlShelahi

You

et al. 2021

IEEE Trans. Sustain. Energy

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We develop an integrative framework to predict the wind power output, considering many uncertainties. For probabilistic wind power forecasts, all the sources of uncertainties arising from both wind speed prediction and wind-to-power conversion process should be collectively addressed. To this end, we model the wind speed using the inhomogeneous geometric Brownian motion and convert the wind speed's prediction density into the wind power density in a closed-form. The resulting wind power density allows us to quantify prediction uncertainties through prediction intervals and to forecast the power that can minimize the expected prediction cost with unequal penalties on the overestimation and underestimation. We evaluate the predictive power of the proposed approach using data from commercial wind farms located in different sites. The results suggest that our approach outperforms alternative approaches in terms of multiple performance measures.

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Section: B Modeling Wind-to-power Conversion Processmentioning

confidence: 99%

Integrative Density Forecast and Uncertainty Quantification of Wind Power Generation

Wang

AlShelahi

You

et al. 2021

IEEE Trans. Sustain. Energy

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“…In particular, we consider the 10-minute maximum flapwise bending moment, which is one of important load types in the wind turbine reliability analysis (Byon, Choe, & Yampikulsakul, 2016;Moriarty, 2008;Yampikulsakul, Byon, Huang, Sheng, & You, 2014). In Choe et al (2015Choe et al ( , 2018, the flapwise bending moments were calculated using the results from the FAST outputs, following the procedure in Moriarty (2008).…”

Section: Wind Turbine Case Studymentioning

confidence: 99%

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Pan

Byon

et al. 2020

Naval Research Logistics

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Quantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.

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“…Finally, we additionally consider another highly volatile process, wind speed. Because of environmental considerations, wind power, as a renewable source of energy, has been increasingly adopted worldwide [23]. Intermittent output of the farm is considered a challenging issue in terms of integrating the wind power into electric power grids.…”

Section: Wind Speed Datamentioning

confidence: 99%

Data-driven prediction for volatile processes based on real option theories

AlShelahi

Wang

You

et al. 2020

International Journal of Production Economics

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This paper presents a new prediction model for time series data by integrating a time-varying Geometric Brownian Motion model with a pricing mechanism used in financial engineering. Typical time series models such as Auto-Regressive Integrated Moving Average assumes a linear correlation structure in time series data. When a stochastic process is highly volatile, such an assumption can be easily violated, leading to inaccurate predictions. We develop a new prediction model that can flexibly characterize a time-varying volatile process without assuming linearity. We formulate the prediction problem as an optimization problem with unequal overestimation and underestimation costs. Based on real option theories developed in finance, we solve the optimization problem and obtain a predicted value, which can minimize the expected prediction cost. We evaluate the proposed approach using multiple datasets obtained from real-life applications including manufacturing, finance, and environment. The numerical results demonstrate that the proposed model shows competitive prediction capability, compared with alternative approaches.

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Adaptive Learning in Time-Variant Processes With Application to Wind Power Systems

Cited by 35 publications

References 23 publications

Integrative Density Forecast and Uncertainty Quantification of Wind Power Generation

Integrative Density Forecast and Uncertainty Quantification of Wind Power Generation

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Data-driven prediction for volatile processes based on real option theories

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