Abstract
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.
Highlights
Wind energy is growing worldwide as a major source of green energy
We evaluate the accuracy of the 5 min forecast of wind speeds based on the described advection techniques and compare it with the results of the statistical methods persistence and autoregressive integrated moving average (ARIMA)
This paper evaluated the use of wind lidar observations for a very short-term forecast of near-coastal winds, using wind speed advection-based models
Summary
Wind energy is growing worldwide as a major source of green energy. In 2017 Denmark produced a record 43.4 % of the country’s electricity with wind energy (Danish Wind Industry Association, DWIA). 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. As an example, Larson and Westrick (2006) used off-site observations in the vicinity of a wind farm in north-eastern Oregon as input variables in different forecast models such as neural networks and support vector machines They showed that the integration of real-time offsite observations significantly improves the forecasting accuracy of those algorithms. A 4 m s−1 wind speed could be observed by a lidar located in a wind farm 3.6 km upstream and 15 min ahead, predicting the start of power generation.
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