Short term wind and energy prediction for offshore wind farms using neural networks

Balluff, Stefan; Bendfeld, Jörg; Krauter, Stefan

doi:10.1109/icrera.2015.7418440

Cited by 31 publications

(9 citation statements)

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“…In a similar fashion, Bessac et al ( 2019) highlighted that subgridscale sea surface fluxes are better represented using stochastic Gaussian process models than deterministic bulk flux parameterizations. Other than model parameterizations, ML devices have also been used to directly forecast offshore wind power by training neural network models on metocean reanalysis data (Balluff et al, 2015) or on in situ supervisory control and data acquisition (SCADA) measurements (Lin et al, 2020).…”

Section: Applications Of Machine Learningmentioning

confidence: 99%

Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer

et al. 2022

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Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.

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Section: Applications Of Machine Learningmentioning

confidence: 99%

Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer

et al. 2022

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“…In a similar fashion, Bessac et al (2019) highlighted that sub-grid scale sea surface fluxes are better represented using stochastic Gaussian process models than deterministic bulk flux parameterizations. Other than model parameterizations, ML devices have also been used to directly forecast offshore wind power by training neural network models on metocean reanalysis data (Balluff et al 2015) or on in situ supervisory control and data acquisition (SCADA) measurements (Lin et al 2020).…”

Section: Applications Of Machine Learningmentioning

confidence: 99%

Scientific Challenges to Characterizing the Wind Resource in the Marine Atmospheric Boundary Layer

Shaw

Berg

Debnath

et al. 2022

Preprint

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Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Post-construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated time) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly non-linear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly non-stationary conditions, which occur with both extratropical storms (e.g., nor’easters) and tropical storms; air-sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps

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“…Wind speed forecasting can be distinguished into very-short (seconds to 30 min), short (up to 6 hrs), medium (up to a day), long (up to a week), and very-long term forecasts. Most of the referenced methods use as input the timeseries of wind speed measurements, with AI models usually relying on recurrent neural networks (RNN) [1], such as LSTM or Elmann RNN [21], meant to capture time-dependent phenomena. One RNN for hourly forecasting in a 6-hr horizon with 12-hr input achieved a MAE of 1.18 m/s but used a testing period of only 45 days [7].…”

Section: Previous Workmentioning

confidence: 99%

Spatio-temporal deep learning for day-ahead wind speed forecasting relying on WRF predictions

et al. 2021

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We focus on deep learning algorithms, improving upon the Weather Research and Forecasting (WRF) model, and we show that the combination of these methods produces day-ahead wind speed predictions of high accuracy, with no need for previous-day measurements. We also show that previous-day data, if available, offer a significant enhancement. Our main contribution is the design and testing of original neural networks that capture both spatial and temporal characteristics of the wind, by combining convolutional (CNN) as well as recurrent (RNN) neural networks. The input predictions are obtained by a WRF model that we appropriately parameterize; we also specify a grid adapted to each park so as to capture its topography. Training uses historical data from 5 wind farms in Greece, and the 5-month testing period includes winter months, which exhibit the highest wind speed values. Our models improve WRF accuracy on average by 19.4%, and the improvement occurs in every month; expectedly, the improvement is lowest for the park where WRF performs best. Our neural network is competitive to state-of-the-art models, achieving an average MAE of 1.75 m/s. Accuracy improves for speed values up to 20 m/s, which are important in wind energy prediction. We also develop

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Short term wind and energy prediction for offshore wind farms using neural networks

Cited by 31 publications

References 1 publication

Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer

Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer

Scientific Challenges to Characterizing the Wind Resource in the Marine Atmospheric Boundary Layer

Spatio-temporal deep learning for day-ahead wind speed forecasting relying on WRF predictions

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