Conference Paper

Short-term wind power forecasting with WRF-ARW model and genetic programming

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Abstract

Forecasting wind power in the short-term usually involves the use of numerical weather prediction models. These models need to run at very high resolutions to provide the best forecasts possible. Producing high resolution forecasts is resource and time consuming, which can be a problem when the forecasts need to be available for the grid operator on the day-ahead. This paper introduces a novel approach for short-term wind power prediction by combining the Weather Research and Forecasting - Advanced Research WRF model (WRF- ARW) with genetic programming, using the latter one for final downscaling and prediction technique, estimating the total hourly power output on the day ahead at a wind farm located in Galicia, Spain.

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... Some studies involved models that are a combination of physical and statistical methods, also referred to as hybrid methods, to predict wind characteristics. For instance, Che et al. [19] utilized the Kalman filter, and Martínez-Arellano et al. [20] employed a genetic algorithm to clean out the physical model output for wind farms in Awaji, Japan, and Satavento, Galicia, respectively. These studies include the forecasting time horizon ranging from 24 to 72 h however did not consider the wake effects of wind turbines while forecasting wind characteristics. ...
... 1-12, 14-15, 33) of wind turbines is less influenced by the upstream wind farms wakes than the Set B (turbines no. 13,[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Three wind turbines from each row in both sets were selected to analyze the wind speed and power variation experienced over seven days in June and January. ...
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Forecasting skills for a wind farm would significantly degrade if the complex wake effects of the upstream wind farms are excluded, especially when they are spatially close to each other. In this study, the Weather Research and Forecasting (WRF) model has been used to predict wind speed and power for a wind farm in Pakistan in the presence of wake interference from neighboring wind farms for two different seasons. Forecasting is done for two different cases i.e. without and with inter-farm wake effects, and different statistical error parameters were evaluated based on the real observations. A significant reduction in errors was observed in the latter case. For instance, the mean absolute errors in wind speed prediction were reduced by 7.7% and 14% in June (summer) and January (winter) respectively, by the inclusion of inter-farm wake effects. Similarly, an improved forecast of power output was obtained by incorporating the interaction of upstream wind farms i.e. a reduction of 15% and 26% in the normalized mean absolute error in power output values was observed for June and January, respectively. However, the prediction accuracy of power output substantially deteriorated in the winter season.
... Some recent studies have been developed about the shortterm predictability of wind speeds with the use of dynamic, mathematical and statistical tools using Numerical Weather Prediction (NWP) (Cheng et al., 2017;Martinez-Arellano and Nolle, 2013), stochastic (Monteiro and Souza, 2013) and hybrid (Camelo et al., 2018b,a;Krishnaveny et al., 2017) models. Ramos et al. (2013) investigated the prediction of hourly wind speeds at 30 m above ground with the atmospheric model WRF (Weather Research Forecasting) for the State of Alagoas-Brazil. ...
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The predictability of wind information in a given location is essential for the evaluation of a wind power project. Predicting wind speed accurately improves the planning of wind power generation, reducing costs and improving the use of resources. This paper seeks to predict the mean hourly wind speed in anemometric towers (at a height of 50 m) at two locations: a coastal region and one with complex terrain characteristics. To this end, the Holt–Winters (HW), Artificial Neural Networks (ANN) and Hybrid time-series models were used. Observational data evaluated by the Modern-Era Retrospective analysis for Research and Applications-Version 2 (MERRA-2) reanalysis at the same height of the towers. The results show that the hybrid model had a better performance in relation to the others, including when compared to the evaluation with MERRA-2. As such, the hybrid models are a good method to forecast wind speed data for wind generation.
... Modifying coefficients from the Kain-Frisch convective scheme in the model can improve the precipitation forecast in a tropical cyclone [6]. Wind speed forecasts can be improved using GP to perform a symbolic regression from a set of past forecasts obtained from the WRF-ARW grid [7]. Venkadesh et al. [8] use genetic algorithms to determine the optimal duration and resolution of prior data for weather variables that was considered a potential input for an ANN model. ...
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The Technical Note series provides an outlet for a variety of NCAR manuscripts that contribute in specialized ways to the body of scientific knowledge but which are not suitable for journal, monograph, or book publication. Reports in this series are issued by the NCAR Scientific Divisions; copies may be obtained on request from the Publications Office of NCAR. Designation symbols for the series include: EDD: IA:
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