Wind sensing demonstration of more than 30km measurable range with a 1.5μm coherent Doppler lidar which has the laser amplifier using Er,Yb:glass planar waveguide

Kameyama, Shumpei; Sakimura, Takeshi; Watanabe, Yojiro; Ando, Toshiyuki; Asaka, Kota; Tanaka, Hisamichi; Yanagisawa, Takayuki; Hirano, Y.; Inokuchi, Hamaki

doi:10.1117/12.977330

Cited by 16 publications

(7 citation statements)

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“…-ARIMA, denoted as ARIMA(p, d, q) is a statistical model widely used in very short-term predictions of wind speeds (Kavasseri and Seetharaman, 2009). It uses recent past values (autoregressive, AR) and recent residuals of the forecast (moving average) to predict current values.…”

Section: Statistical Modelsmentioning

confidence: 99%

“…They are built on relationships developed among historical measurements, assuming that these relationships are also applicable in the future. Examples of statistical methods used to predict wind speed and power can be found in Hill et al (2012) for the autoregressive (AR) model, Torres et al (2005) for the autoregressive moving average (ARMA) model and Kavasseri and Seetharaman (2009) for the autoregressive integrated moving average (ARIMA) model. Torres et al (2005) applied the ARMA model to predict hourly average wind speeds at five weather stations in Navarre, Spain, during different times of the year, with a forecasting horizon from 1 to 10 h. They showed smaller errors for ARMA models compared to those of the persistence method.…”

Section: Introductionmentioning

confidence: 99%

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Very short-term forecast of near-coastal flow using scanning lidars

et al. 2018

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Abstract. Wind measurements can reduce the uncertainty in the prediction of wind energy production. Today, commercially available scanning lidars can scan the atmosphere up to several kilometres. Here, we use lidar measurements to forecast near-coastal winds with lead times of 5 min. Using Taylor's frozen turbulence hypothesis together with local topographic corrections, we demonstrate that wind speeds at a downstream position can be forecast by using measurements from a scanning lidar performed upstream in a very short-term horizon. The study covers 10 periods characterised by neutral and stable atmospheric conditions. Our methodology shows smaller forecasting errors than those of the persistence method and the autoregressive integrated moving average (ARIMA) model. We discuss the applicability of this forecasting technique with regards to the characteristics of the lidar trajectories, the site-specific conditions and the atmospheric stability.

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Section: Statistical Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Very short-term forecast of near-coastal flow using scanning lidars

et al. 2018

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“…These probabilistic forecasts are known as predictive densities or probability distributions and provide important information for making risk-based decisions [15].Over the last two decades, the use of remote sensing measurements such as long-range lidars [16] has been extended in the wind industry. These systems are capable of measuring wind speed and direction (under certain assumptions) up to 30 km [17]. Unlike conventional wind measurements from met-mast or satellites, they present an adequate trade-off between temporal and spatial resolution for wind farm applications.…”

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confidence: 99%

On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

et al. 2018

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Very short-term forecasts of wind power provide electricity market participants with extremely valuable information, especially in power systems with high penetration of wind energy. In very short-term horizons, statistical methods based on historical data are frequently used. This paper explores the use of dual-Doppler radar observations of wind speed and direction to derive five-minute ahead deterministic and probabilistic forecasts of wind power. An advection-based technique is introduced, which estimates the predictive densities of wind speed at the target wind turbine. In a case study, the proposed methodology is used to forecast the power generated by seven turbines in the North Sea with a temporal resolution of one minute. The radar-based forecast outperforms the persistence and climatology benchmarks in terms of overall forecasting skill. Results indicate that when a large spatial coverage of the inflow of the wind turbine is available, the proposed methodology is also able to generate reliable density forecasts. Future perspectives on the application of Doppler radar observations for very short-term wind power forecasting are discussed in this paper. methods can be found in [6][7][8]. Very short-term forecasts of wind speed and power are often based on machine learning methods such as artificial neural networks [9] or Markov chain models [10,11], which are trained with historical data. Alternatively, combinations of different models (known as hybrid models) have been proposed to overcome the deficiencies of single models [12]. Parallel to the development of single point or deterministic forecasting models, the use of probabilistic forecasts, which include information about the uncertainty associated with the predicted events, has increased. Examples of very short-term probabilistic forecasts of wind power using statistical methods can be found in [13,14]. These probabilistic forecasts are known as predictive densities or probability distributions and provide important information for making risk-based decisions [15].Over the last two decades, the use of remote sensing measurements such as long-range lidars [16] has been extended in the wind industry. These systems are capable of measuring wind speed and direction (under certain assumptions) up to 30 km [17]. Unlike conventional wind measurements from met-mast or satellites, they present an adequate trade-off between temporal and spatial resolution for wind farm applications. However, the prediction horizon of a remote sensing-based forecasting model is limited by the maximum range of the remote sensing measurements and also influenced by meteorological conditions [18]. Indeed, publications on the use of long-range lidar measurements for wind energy applications have reported measurements with a maximum range of less than ten kilometers [19][20][21]. Despite those limitations, a recent contribution showed that a lidar-based forecasting technique can provide better results than conventional statistical benchmarks when forecasting near-coastal winds with le...

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“…Later in 2014, MEC reported their airborne test result. This system allowed horizontal detection at greater than 9-km distance, which enables CAT detection 30 s in advance (Kameyama et al, 2012;Sakimura et al, 2013).…”

Section: C-dwl At 15 µMmentioning

confidence: 99%

Doppler Wind Lidar From UV to NIR: A Review With Case Study Examples

Shangguan

Qiu

Yuan

et al. 2022

Front. Remote Sens.

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Doppler wind lidar (DWL) uses the optical Doppler effect to measure atmospheric wind speed with high spatial-temporal resolution and long detection range and has been widely applied in scientific research and engineering applications. With the development of related technology, especially laser and detector technology, the performance of the DWL has significantly improved for the past few decades. DWL utilizes different principles and different tracers to sense the wind speed from the ground to the mesosphere, which leads to the difference in choosing the laser working wavelength. This article will review the working wavelength consideration of DWL, and typical DWLs will present from ultraviolet to near-infrared, after which three typical applications will be shown.

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Wind sensing demonstration of more than 30km measurable range with a 1.5μm coherent Doppler lidar which has the laser amplifier using Er,Yb:glass planar waveguide

Cited by 16 publications

References 0 publications

Very short-term forecast of near-coastal flow using scanning lidars

Very short-term forecast of near-coastal flow using scanning lidars

On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

Doppler Wind Lidar From UV to NIR: A Review With Case Study Examples

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