Wind Power Forecasting

Haupt, Sue Ellen; Mahoney, William P.; Parks, Keith

doi:10.1007/978-1-4614-9221-4_14

Cited by 15 publications

(14 citation statements)

References 28 publications

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“…The AI system takes a two-step process: it first applies dynamic model output statistics, which uses multilinear regression to remove bias from each model individually. It then generates a consensus forecast by using optimization methods to derive weighting coefficients to blend the models [1][2][3]16]. DICast thus constantly improves the forecast by learning from past errors based on comparisons of recent forecasts with observations.…”

Section: Statistical Postprocessingmentioning

confidence: 99%

“…The new augmented and enhanced forecasting system provides capabilities for short-term forecasting, including wind ramp detection, prediction of extreme events such as icing conditions that can significantly impact wind power production when wind resource is abundant, empirical wind-to-power conversion techniques, and uncertainty quantification in power forecasting. This system employs artificial intelligence methods [1][2][3] to integrate disparate data sources with publicly available numerical weather prediction model outputs. The development of the new comprehensive forecasting system is motivated by risk reduction of wind power integration into a power grid and reduction of the levelized cost of wind power.…”

Section: Introductionmentioning

confidence: 99%

“…The schematic diagram of the comprehensive wind power forecasting system [1][2][3] developed by NCAR is shown in Figure 1. The central component of the system is the Dynamic Integrated foreCast System (DICast® ).…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction

et al. 2020

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The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed.

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Section: Statistical Postprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction

et al. 2020

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“…This method is effective at both improving on the deterministic forecast and using the multiple analogs to form an ensemble that can be used to quantify the Alessandrini et al 2015). Accuracy of forecasts has been improving steadily with some areas now seeing single digit errors in terms of percentage of capacity at a wind farm (Orwig et al 2014;Haupt and Mahoney 2015). These improvements have stemmed from including observations in the immediate vicinity of the resource, both in the nowcasting and assimilated into the NWP models, as well as better methods of blending multiple models for the appropriate timescales.…”

Section: Probabilistic Forecasts and The Analog Ensemblementioning

confidence: 99%

Short-Range Forecasting for Energy

Haupt

2018

Weather &Amp; Climate Services for the Energy Industry

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Short-range forecasts for periods on the order of hours to days and up to two weeks ahead are necessary to smoothly run transmission and distribution systems, plan maintenance, protect infrastructure and allocate units. In particular, forecasting the renewable energy resources on a day-to-day basis enables integration of increasing capacities of these variable resources. This chapter describes the basics of this short-range forecasting, beginning with the observation-based "nowcasting" of the first 15 minutes and ranging up to two weeks using numerical weather prediction. We discuss how blending multiple forecasts can increase accuracy and how probabilistic forecasts are being used to quantify the forecast uncertainty.

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“…Those observations then become the analog ensemble. This method is effective at both improving on the deterministic forecast and using the multiple analogs to form an ensemble that can be used to quantify the Accuracy of forecasts has been improving steadily with some areas now seeing single digit errors in terms of percentage of capacity at a wind farm (Orwig et al 2014;Haupt and Mahoney 2015). These improvements have stemmed from including observations in the immediate vicinity of the resource, both in the nowcasting and assimilated into the NWP models, as well as better methods of blending multiple models for the appropriate timescales.…”

Section: Probabilistic Forecasts and The Analog Ensemblementioning

confidence: 99%

Weather & Climate Services for the Energy Industry

Troccoli¹

2018

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Wind Power Forecasting

Cited by 15 publications

References 28 publications

A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction

A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction

Short-Range Forecasting for Energy

Weather & Climate Services for the Energy Industry

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