J0503-1-5 Development and Evaluation of a Prediction System for Wind Power Generation over a Wind Farm

Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. In addition, the model was calibrated for five validation percentages (5% to 25% in 5% intervals) under each algorithm to identify the best training algorithm with the most suitable training and validation percentages. Mean squared error (MSE), coefficient of correlation (R), root mean squared error ratio (RSR), Nash number, and BIAS were used to evaluate the performance of the developed ANN models. Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R > 0.91, MSE < 0.22, and BIAS < 1. Among them, the LM training algorithm at 70% of training and 5% of validation percentages produces the best forecasting results. The developed models can be effectively used in the prediction of wind power at the Pawan Danawi wind farm. In addition, the models can be used with the projected climatic scenarios in predicting the future wind power harvest. Furthermore, the models can acceptably be used in similar environmental and climatic conditions to identify the wind power potential of the area.

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Wind power generation forecast by coupling numerical weather prediction model and gradient boosting machines in Yahyalı wind power plant

Wind Engineering ◽

10.1177/0309524x20972115 ◽

2020 ◽

pp. 0309524X2097211

Author(s):

Cem Özen ◽

Umur Dinç ◽

Ali Deniz ◽

Haldun Karan

Keyword(s):

Power Generation ◽

Wind Power ◽

Hybrid Model ◽

Spatial Resolution ◽

Numerical Weather Prediction ◽

Wind Farm ◽

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Wind Power Generation ◽

Gradient Boosting

Forecasting of the wind speed and power generation for a wind farm has always been quite challenging and has importance in terms of balancing the electricity grid and preventing energy imbalance penalties. This study focuses on creating a hybrid model that uses both numerical weather prediction model and gradient boosting machines (GBM) for wind power generation forecast. Weather Research and Forecasting (WRF) model with a low spatial resolution is used to increase temporal resolutions of the computed new or existing variables whereas GBM is used for downscaling purposes. The results of the hybrid model have been compared with the outputs of a stand-alone WRF which is well configured in terms of physical schemes and has a high spatial resolution for Yahyalı wind farm over a complex terrain located in Turkey. Consequently, the superiority of the hybrid model in terms of both performance indicators and computational expense in detail is shown.

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Discussion of “Prediction Intervals for Short-Term Wind Farm Generation Forecasts” and “Combined Nonparametric Prediction Intervals for Wind Power Generation”

IEEE Transactions on Sustainable Energy ◽

10.1109/tste.2014.2323851 ◽

2014 ◽

Vol 5 (3) ◽

pp. 1019-1020 ◽

Cited By ~ 14

Author(s):

Pierre Pinson ◽

Julija Tastu

Keyword(s):

Power Generation ◽

Wind Power ◽

Wind Farm ◽

Wind Power Generation ◽

Prediction Intervals ◽

Short Term ◽

Nonparametric Prediction ◽

Nonparametric Prediction Intervals

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The Study of Short-Term Wind Power Generation Prediction Methods Based on Clustering Analysis in Wind Farm

Sustainable Energy ◽

10.12677/se.2015.53003 ◽

2015 ◽

Vol 05 (03) ◽

pp. 17-23

Author(s):

存董

Keyword(s):

Power Generation ◽

Wind Power ◽

Clustering Analysis ◽

Wind Farm ◽

Wind Power Generation ◽

Prediction Methods ◽

Short Term

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Structural Control Issues in New Generation Offshore Wind Energy Plants

Advances in Science and Technology ◽

10.4028/www.scientific.net/ast.83.167 ◽

2012 ◽

Vol 83 ◽

pp. 167-176 ◽

Cited By ~ 1

Author(s):

Ning Su Luo

Keyword(s):

Power Generation ◽

Wind Power ◽

Wind Turbines ◽

Structural Control ◽

Wind Farm ◽

Wind Power Generation ◽

Offshore Wind ◽

Wave Loads ◽

Offshore Wind Power ◽

New Generation

A new constructive solution for the offshore wind power generation is to use floating wind turbines. An offshore wind farm situated sufficiently far away from the coast can generate more wind power and will have a longer operation life since the wind is stronger and more consistent than that on or near the coast. One of the main challenges is to reduce the fatigue of a floating wind turbine so as to guarantee its proper functioning under the constraints imposed by the floating support platforms. This paper will discuss the structural control issues related to the mitigation of dynamic wind and wave loads on the floating wind turbines so as to enhance the offshore wind power generation.

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Estimating Wind Farm Transformers Rating through Lifetime Characterization Based on Stochastic Modeling of Wind Power

Energies ◽

10.3390/en14051498 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1498

Author(s):

Maurizio Fantauzzi ◽

Davide Lauria ◽

Fabio Mottola ◽

Daniela Proto

Keyword(s):

Power Generation ◽

Wind Power ◽

Wind Farm ◽

Wind Farms ◽

Mineral Oil ◽

Wind Power Generation ◽

Numerical Application ◽

Stochastic Characterization ◽

Large Wind

This paper deals with the problem of the optimal rating of mineral-oil-immersed transformers in large wind farms. The optimal rating is derived based on the probabilistic analyses of wind power generation through the Ornstein–Uhlenbeck stochastic process and on thermal model of the transformer through the integration of stochastic differential equations. These analyses allow the stochastic characterization of lifetime reduction of the transformer and then its optimal rating through a simple closed form. The numerical application highlights the effectiveness and easy applicability of the proposed methodology. The proposed methodology allows deriving the rating of transformers which better fits the specific peculiarities of wind power generation. Compared to the conventional approaches, the proposed method can better adapt the transformer size to the intermittence and variability of the power generated by wind farms, thus overcoming the often-recognized reduced lifetime.

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Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada

Energies ◽

10.3390/en12101998 ◽

2019 ◽

Vol 12 (10) ◽

pp. 1998 ◽

Cited By ~ 1

Author(s):

Yilan Luo ◽

Deniz Sezer ◽

David Wood ◽

Mingkuan Wu ◽

Hamid Zareipour

Keyword(s):

Power Generation ◽

Wind Power ◽

Wind Farm ◽

Wind Farms ◽

Gaussian Random Fields ◽

Wind Power Generation ◽

Temporal Correlations ◽

Prevailing Wind Direction ◽

Full Symmetry ◽

Spatio Temporal

This paper describes a hierarchy of increasingly complex statistical models for wind power generation in Alberta applied to wind power production data that are publicly available. The models are based on combining spatial and temporal correlations. We apply the method of Gaussian random fields to analyze the wind power time series of the 19 existing wind farms in Alberta. Following the work of Gneiting et al., three space-time models are used: Stationary, Separability, and Full Symmetry. We build several spatio-temporal covariance function estimates with increasing complexity: separable, non-separable and symmetric, and non-separable and non-symmetric. We compare the performance of the models using kriging predictions and prediction intervals for both the existing wind farms and a new farm in Alberta. It is shown that the spatial correlation in the models captures the predominantly westerly prevailing wind direction. We use the selected model to forecast the mean and the standard deviation of the future aggregate wind power generation of Alberta and investigate new wind farm siting on the basis of reducing aggregate variability.

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On the wake effect in wind farm power forecasting: a new data-driven approach

E3S Web of Conferences ◽

10.1051/e3sconf/202019708016 ◽

2020 ◽

Vol 197 ◽

pp. 08016

Author(s):

Fabio Famoso ◽

Sebastian Brusca ◽

Antonio Galvagno ◽

Michele Messina ◽

Rosario Lanzafame

Keyword(s):

Power Generation ◽

Wind Power ◽

Wind Farm ◽

Wind Farms ◽

Wind Power Generation ◽

Physical Models ◽

Production Forecast ◽

Innovative Method ◽

Power Forecasting

Wind power generation differs from other energy sources, such as thermal, solar or hydro, due to the inherent stochastic nature of wind. For this reason wind power forecasting, especially for wind farms, is a complex task that cannot be accurately solved with traditional statistical methods or needs large computational systems if physical models are used. Recently, the so-called learning approaches are considered a good compromise among the previous methods since they are able to integrate physical phenomena such as wake effects without presenting heavy computational loads. The present work deals with an innovative method to forecast wind power generation in a wind farm with a combination of GISbased methods, neural network approach and a wake physical model. This innovative method was tested with a wind farm located in Sicily (Italy), used as a case study. It consists of 30 identical wind turbines (850 kW each one), located at different heights, for an overall Power peak of 25 MW. The time series dataset consists of one year with a sampling time of 10 minutes considering wind speeds and wind directions. The output of this innovative model leaded to good results, especially for medium-term overall energy production forecast for the case study.

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