Using Conditional Kernel Density Estimation for Wind Power Density Forecasting

Jeon, Junho; Taylor, James W.

doi:10.1080/01621459.2011.643745

Cited by 187 publications

(126 citation statements)

References 34 publications

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“…The kernel-based method appears to be a capable statistical modeling tool, not only capturing the complicated higher order interaction effects but also avoiding the need to specify a functional form of the power curve relationship. Indeed, a bivariate CKD including wind speed and direction was used by Jeon and Taylor (2012), which has produced encouraging improvement. We also believe that the aforementioned physical understanding behind wind power generation offers useful clues.…”

Section: Additive Multivariate Kernel-based Power Curve Modelmentioning

confidence: 99%

“…Regression Trees (Chipman, George and McCulloch 2010, BART) and the Smoothing Spline ANOVA (Gu 2013, SSANOVA), as well as the kernel-based method, as used by Jeon and Taylor (2012). When we look for a modeling strategy, we settle on the kernel-based approach, namely, using a conditional kernel density estimation (CKD) (Rosenblatt 1969;Hyndman, Bashtannyk and Grunwald 1996) for estimating the condition density, p(y|x), or a kernel regression (Nadaraya 1964;Watson 1964) for estimating the conditional expectation, E(y|x).…”

Section: Additive Multivariate Kernel-based Power Curve Modelmentioning

confidence: 99%

“…A handful of studies do explicitly include both wind speed and wind direction in their models (Nielsen, Nielsen and Madsen 2002;Sanchez 2006;Pinson, Nielsen, Madsen and Nielsen 2008;Jeon and Taylor 2012). The inclusion of wind direction is not surprising because of the physical intuition that how wind blows the turbine should matter in wind power production.…”

Section: Introductionmentioning

confidence: 99%

“…The specific approaches employed in these studies differed: Nielsen et al (2002) used a local polynomial regression; Sanchez (2006) presented a dynamic combination of several prediction models based on time-varying coefficients and a recursive solution procedure; Pinson et al (2008) used a total least squares criterion (i.e., orthogonal distance least squares), together with a Huber M-estimator, to achieve a certain degree of robustness. Kernel methods are among the sophisticated approaches that are used to model wind direction predictions (Marzio, Panzera and Taylor 2012;Marzio, Panzera and Taylor 2013;Marzio, Panzera and Taylor 2014) and model the power to wind speed/direction relationship through Jeon and Taylor (2012). Not only does Jeon and Taylor (2012) consider both wind speed and wind direction, it also produces a density estimation that can be used to account for uncertainty in wind power prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Kernel methods are among the sophisticated approaches that are used to model wind direction predictions (Marzio, Panzera and Taylor 2012;Marzio, Panzera and Taylor 2013;Marzio, Panzera and Taylor 2014) and model the power to wind speed/direction relationship through Jeon and Taylor (2012). Not only does Jeon and Taylor (2012) consider both wind speed and wind direction, it also produces a density estimation that can be used to account for uncertainty in wind power prediction.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms

Lee¹,

Ding²,

Genton³

et al. 2015

Journal of the American Statistical Association

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In the wind industry, a power curve refers to the functional relationship between the power output generated by a wind turbine and the wind speed at the time of power generation.Power curves are used in practice for a number of important tasks including predicting wind power production and assessing a turbine's energy production efficiency. Nevertheless, actual wind power data indicate that the power output is affected by more than just wind speed.Several other environmental factors, such as wind direction, air density, humidity, turbulence intensity, and wind shears, have potential impact. Yet, in industry practice, as well as in the literature, current power curve models primarily consider wind speed and, sometimes, wind speed and direction. We propose an additive multivariate kernel method that can include the aforementioned environmental factors as a new power curve model. Our model provides, conditional on a given environmental condition, both the point estimation and density estimation of power output. It is able to capture the nonlinear relationships between environmental factors and the wind power output, as well as the high-order interaction effects among some of the environmental factors. Using operational data associated with four turbines in an inland wind farm and two turbines in an offshore wind farm, we demonstrate the improvement achieved by our kernel method.

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Section: Additive Multivariate Kernel-based Power Curve Modelmentioning

confidence: 99%

Section: Additive Multivariate Kernel-based Power Curve Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms

Lee¹,

Ding²,

Genton³

et al. 2015

Journal of the American Statistical Association

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Evaluating the impacts of climate change on diurnal wind power cycles using multiple regional climate models

et al. 2015

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Electrical utility system operators must plan resources so that electricity supply matches demand throughout the day. As the proportion of wind‐generated electricity in the US grows, changes in daily wind patterns have the potential either to disrupt the utility or increase the value of wind to the system over time. Wind power projects are designed to last many years, so at this timescale, climate change may become an influential factor on wind patterns. We examine the potential effects of climate change on the average diurnal power production cycles at 12 locations in North America by analyzing averaged and individual output from nine high‐resolution regional climate models comprising historical (1971–1999) and future (2041–2069) periods. A semi‐parametric mixed model is fit using cubic B‐splines, and model diagnostics are checked. Then, a likelihood ratio test is applied to test for differences between the time periods in the seasonal daily averaged cycles, and agreement among the individual regional climate models is assessed. We investigate the significant changes by combining boxplots with a differencing approach and identify broad categories of changes in the amplitude, shape, and position of the average daily cycles. We then discuss the potential impact of these changes on wind power production. Copyright © 2015 John Wiley & Sons, Ltd.

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Infectious disease prediction with kernel conditional density estimation

Ray

Sakrejda

Lauer

et al. 2017

Statistics in Medicine

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Creating statistical models that generate accurate predictions of infectious disease incidence is a challenging problem whose solution could benefit public health decision makers. We develop a new approach to this problem using kernel conditional density estimation (KCDE) and copulas. We obtain predictive distributions for incidence in individual weeks using KCDE and tie those distributions together into joint distributions using copulas. This strategy enables us to create predictions for the timing of and incidence in the peak week of the season. Our implementation of KCDE incorporates 2 novel kernel components: a periodic component that captures seasonality in disease incidence and a component that allows for a full parameterization of the bandwidth matrix with discrete variables. We demonstrate via simulation that a fully parameterized bandwidth matrix can be beneficial for estimating conditional densities. We apply the method to predicting dengue fever and influenza and compare to a seasonal autoregressive integrated moving average model and HHH4, a previously published extension to the generalized linear model framework developed for infectious disease incidence. The KCDE outperforms the baseline methods for predictions of dengue incidence in individual weeks. The KCDE also offers more consistent performance than the baseline models for predictions of incidence in the peak week and is comparable to the baseline models on the other prediction targets. Using the periodic kernel function led to better predictions of incidence. Our approach and extensions of it could yield improved predictions for public health decision makers, particularly in diseases with heterogeneous seasonal dynamics such as dengue fever.

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Using Conditional Kernel Density Estimation for Wind Power Density Forecasting

Cited by 187 publications

References 34 publications

Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms

Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms

Evaluating the impacts of climate change on diurnal wind power cycles using multiple regional climate models

Infectious disease prediction with kernel conditional density estimation

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