Influence of the reliability of short-term electrical power forecasting for a wind farm on the generation cost per MWh. A case study in the Canary Islands

2015 ◽  
pp. 168-173
Author(s):  
Ulises Portero ◽  
Sergio Velázquez ◽  
María Miranda
Keyword(s):  
Canary Islands ◽  
Wind Farm ◽  
Electrical Power ◽  
Short Term ◽  
Generation Cost ◽  
Power Forecasting

scholarly journals Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Zhongxian Men ◽  
Eugene Yee ◽  
Fue-Sang Lien ◽  
Zhiling Yang ◽  
Yongqian Liu
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Northern China ◽  
Short Term ◽  
Artificial Neural ◽  
Nonlinear Autoregressive ◽  
Power Forecasting

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

scholarly journals Short-term Power Forecasting Method and Application of Wind Farm

2018 ◽  
Vol 232 ◽  
pp. 04001
Author(s):  
Xiaohu Yang ◽  
Rong Ju ◽  
Zhe Yuan ◽  
Zhenya Zhang
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Normal Operation ◽  
Prediction Methods ◽  
Practical Application ◽  
Short Term ◽  
Forecasting Method ◽  
Wind Power System ◽  
Power Forecasting

The prediction of output power of wind farm has important value and significance to the normal operation of some large-scale wind power system. In this paper, the related prediction methods and practical application are studied, and the short-term power forecasting method of the wind power of the vector machine-Markov chain is proposed.

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

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.

A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

2021 ◽  
Vol 236 ◽  
pp. 114002
Author(s):  
Mehdi Neshat ◽  
Meysam Majidi Nezhad ◽  
Ehsan Abbasnejad ◽  
Seyedali Mirjalili ◽  
Lina Bertling Tjernberg ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Wind Farm ◽  
Evolutionary Model ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Short Term ◽  
Wind Speed Forecasting

Mathematical Programming Based Short Term Load Forecasting Algorithm, Case Study: Turkey 2010

2012 ◽  
Vol 433-440 ◽  
pp. 3934-3938
Author(s):  
Nurettin Çetinkaya
Keyword(s):  
Mathematical Programming ◽  
Power Systems ◽  
Computational Algorithm ◽  
Electrical Power ◽  
Load Forecasting ◽  
Short Term ◽  
Electrical Power Systems ◽  
Short Term Load Forecasting ◽  
The Mean

Short-term load forecasting (STLF) is an important problem in the operation of electrical power generation and transmission. In this paper, STLF algorithm was developed for electrical power systems using mathematical programming with Matlab. A fast and efficient computational algorithm has been obtained for STLF. The mean absolute percentage errors (MAPE) of daily loads forecast and weekly loads forecast for Turkey are found as 1,76%, 1,92%, respectively.

scholarly journals Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6360
Author(s):  
Georgios Gasparis ◽  
Wai Hou Lio ◽  
Fanzhong Meng
Keyword(s):  
Neural Network ◽  
Gaussian Process ◽  
Wind Farm ◽  
Electrical Power ◽  
Control Design ◽  
Gaussian Process Regression ◽  
Surrogate Models ◽  
The Other ◽  
Short Term ◽  
Different Types

Fatigue damage of turbine components is typically computed by running a rain-flow counting algorithm on the load signals of the components. This process is not linear and time consuming, thus, it is non-trivial for an application of wind farm control design and optimisation. To compensate this limitation, this paper will develop and compare different types of surrogate models that can predict the short term damage equivalent loads and electrical power of wind turbines, with respect to various wind conditions and down regulation set-points, in a wind farm. More specifically, Linear Regression, Artificial Neural Network and Gaussian Process Regression are the types of the developed surrogate models in this work. The results showed that Gaussian Process Regression outperforms the other types of surrogate models and can effectively estimate the aforementioned target variables.

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