Exergy analysis in a wind speed prognostic model as a wind farm sitting selection tool: A case study in Southern Greece

Xydis, George; Koroneos, C.; Loizidou, M.

doi:10.1016/j.apenergy.2009.03.017

Cited by 72 publications

(27 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This issue is of particular importance for wind power [3][4][5]. The stochastic nature of wind and of meteorological conditions, with the consequent discontinuity of the production of wind energy, entails serious problems for the use of the resulting electrical energy in distribution networks.…”

Section: Introductionmentioning

confidence: 99%

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

et al. 2014

View full text Add to dashboard Cite

A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP). A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM) with Wavelet Decomposition (WD) were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN)-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

et al. 2014

View full text Add to dashboard Cite

show abstract

“…In recent literatures [16][17][18][19][20][21][22], the choice and number of principal parameters considered for parametric study of wind farms were made purely on design constraints. But, in this study parameters influencing the wake decay effects were directly considered.…”

Section: Single Column Array With Four Turbinesmentioning

confidence: 99%

Exergy Analysis of Single Array Wind Farm Using Wake Effects

Saravanan¹,

Karthikeyan

Samuel³

2011

ENG

View full text Add to dashboard Cite

The influence of wake parameters on the exergy analysis of single array wind farm is studied in this paper. Key parameters which influence wake effects in a wind farm are wind velocity, tip speed ratio, number of blades, rotor speed, rotor diameter and hub height. Three different models namely power, wake and exergy model were used in estimating the exergy efficiency of the single array wind farm. Even though it is ideal for wind farms to fix the wind turbines in rows and columns the conditions of the site may not always be conducive for it. Hence analysis has been done keeping the wind turbines at random in a row and the effect of positioning on the performance is analyzed. Energy and exergy efficiency calculations were made for different cases by varying the positions of wind turbines in the single array wind farm. Standard relations were used in estimating the energy deficit in the wind farm due to wake effects. The wake effects were found to have an aggregated influence on the energy production of the wind farm, which results from the changes in the key parameters mentioned above. Potential areas for reducing energy losses by proper location and selection of turbines based on rating are highlighted. The influence of individual parameters contributing to the wake effect were analyzed and discussed in detail.

show abstract

“…A thorough assessment of wind energy potential in the ECS must be conducted before building a wind farm. A detailed assessment in promising locations should be subsequently performed [19]. Limited studies assessed wind energy potential in the ECS.…”

Section: Introductionmentioning

confidence: 99%

On the Wind Energy Resource and Its Trend in the East China Sea

Osinowo

Lin

Zhao

et al. 2017

Journal of Renewable Energy

View full text Add to dashboard Cite

This study utilizes a 30-year (1980-2009) 10 m wind field dataset obtained from the European Center for Medium Range Weather Forecast to investigate the wind energy potential in the East China Sea (ECS) by using Weibull shape and scale parameters. The region generally showed good wind characteristics. The calculated annual mean of the wind power resource revealed the potential of the region for large-scale grid-connected wind turbine applications. Furthermore, the spatiotemporal variations showed strong trends in wind power in regions surrounding Taiwan Island. These regions were evaluated with high wind potential and were rated as excellent locations for installation of large wind turbines for electrical energy generation. Nonsignificant and negative trends dominated the ECS and the rest of the regions; therefore, these locations were found to be suitable for small wind applications. The wind power density exhibited an insignificant trend in the ECS throughout the study period. The trend was strongest during spring and weakest during autumn.

show abstract

Exergy analysis in a wind speed prognostic model as a wind farm sitting selection tool: A case study in Southern Greece

Cited by 72 publications

References 52 publications

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

Exergy Analysis of Single Array Wind Farm Using Wake Effects

On the Wind Energy Resource and Its Trend in the East China Sea

Contact Info

Product

Resources

About