Automatic Classification of Offshore Wind Regimes With Weather Radar Observations

Trombe, Pierre‐Julien; Madsen, Henrik

doi:10.1109/jstars.2013.2252604

Cited by 15 publications

(8 citation statements)

References 32 publications

(51 reference statements)

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“…15,50 Quantitative analyses based on datasets including information weather radar and offshore wind farms may require fairly advanced image analysis and statistical methods, since it may not be straightforward to link simple descriptors of precipitation contents in the images to local variance estimates in wind speed time-series. As recently shown in the work of Trombe, Pinson and Madsen, 16 more advanced descriptors need to be defined, hence accounting for the potential effect of, e.g., the motion and type of precipitation systems or the season of the year implying various types of mesoscale phenomena. Our main objective here was to present how the datasets necessary for such quantitative analyses may be obtained, also describing the perspectives and potential limitations from considering different types of weather radar technologies.…”

Section: Resultsmentioning

confidence: 99%

“…For a more quantitative analysis based on such information collected in the frame of this experiment, the reader is referred to. 16 It is shown there how the various attributes of precipitation systems (type, speed and direction of motion, intensity, etc.) may be summarized by a number of indicators, and then linked to various types of wind speed and power fluctuations, characterized by their dynamic features and overall magnitude.…”

Section: Introductionmentioning

confidence: 99%

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Weather radars – the new eyes for offshore wind farms?

et al. 2013

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Offshore wind fluctuations are such that dedicated prediction and control systems are needed for optimizing the management of wind farms in real‐time. In this paper, we present a pioneer experiment – Radar@Sea – in which weather radars are used for monitoring the weather at the Horns Rev offshore wind farm, in the North Sea. First, they enable the collection of meteorological observations at high spatio‐temporal resolutions for enhancing the understanding of meteorological phenomena that drive wind fluctuations. And second, with the extended visibility they offer, they can provide relevant inputs to prediction systems for anticipating changes in the wind fluctuation dynamics, generating improved wind power forecasts and developing specific control strategies. However, integrating weather radar observations into automated decision support systems is not a plug‐and‐play task, and it is important to develop a multi‐disciplinary approach linking meteorology and statistics. Here, (i) we describe the settings of the Radar@Sea experiment, (ii) we report the experience gained with these new remote sensing tools, (iii) we illustrate their capabilities with some concrete meteorological events observed at Horns Rev and (iv) we discuss the future perspectives for weather radars in wind energy. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weather radars – the new eyes for offshore wind farms?

et al. 2013

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“…In parallel, considering novel remote sensing inputs to renewable energy forecasting, such as from sky imagers and radars, these require a wealth of methodological developments inspired by, for example, image analysis in order to define, extract and use relevant features from those images. Some may be directly informed by expert knowledge, but the most likely data-driven approaches are those which will be able to readily obtain the required features directly from analysis of the input images, as for the example of weather systems in the vicinity of offshore wind farms (Trombe, Pinson, & Madsen, 2014).…”

Section: Advances In Very Short-term Forecastingmentioning

confidence: 99%

The future of forecasting for renewable energy

Sweeney

Bessa

Browell

et al. 2019

WIREs Energy & Environment

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Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state‐of‐the‐art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Wind Power > Systems and Infrastructure Photovoltaics > Systems and Infrastructure

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“…They are consequently able to anticipate precipitation fields associated with severe wind speed and power fluctuations with lead times of minutes to few hours, as they can measure up to few hundreds of kilometers, depending on the radar type. The capabilities of anticipating strong wind power fluctuations in offshore wind farms using local weather radars was introduced in [63,64]. In their work the authors were able to track the arrival of precipitation events to the surroundings of an offshore wind farm.…”

Section: Weather Radars For Prediction Of Strong Wind Power Fluctuationsmentioning

confidence: 99%

“…These are termed regime-switching models and can be based on unobserved regimes [78,79] or by observed regimes like atmospheric conditions [80,81]. It follows that these regimes can be derived from lidar/radar measurements [64]. The benefit of regime switching is that the statistical models can react faster to changing conditions, as opposed to having a fixed coefficient models or by tracking slower changes in behaviour via for instance an online update of the coefficient estimates.…”

Section: Statistical Time Series Modelsmentioning

confidence: 99%

Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36

et al. 2019

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The demand for minute-scale forecasts of wind power is continuously increasing with the growing penetration of renewable energy into the power grid, as grid operators need to ensure grid stability in the presence of variable power generation. For this reason, IEA Wind Tasks 32 and 36 together organized a workshop on “Very Short-Term Forecasting of Wind Power” in 2018 to discuss different approaches for the implementation of minute-scale forecasts into the power industry. IEA Wind is an international platform for the research community and industry. Task 32 tries to identify and mitigate barriers to the use of lidars in wind energy applications, while IEA Wind Task 36 focuses on improving the value of wind energy forecasts to the wind energy industry. The workshop identified three applications that need minute-scale forecasts: (1) wind turbine and wind farm control, (2) power grid balancing, (3) energy trading and ancillary services. The forecasting horizons for these applications range from around 1 s for turbine control to 60 min for energy market and grid control applications. The methods that can be applied to generate minute-scale forecasts rely on upstream data from remote sensing devices such as scanning lidars or radars, or are based on point measurements from met masts, turbines or profiling remote sensing devices. Upstream data needs to be propagated with advection models and point measurements can either be used in statistical time series models or assimilated into physical models. All methods have advantages but also shortcomings. The workshop’s main conclusions were that there is a need for further investigations into the minute-scale forecasting methods for different use cases, and a cross-disciplinary exchange of different method experts should be established. Additionally, more efforts should be directed towards enhancing quality and reliability of the input measurement data.

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Automatic Classification of Offshore Wind Regimes With Weather Radar Observations

Cited by 15 publications

References 32 publications

Weather radars – the new eyes for offshore wind farms?

Weather radars – the new eyes for offshore wind farms?

The future of forecasting for renewable energy

Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36

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