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Supervised machine learning in wind forecasting and ramp event prediction /
"[A]n up to date overview of the broad area of wind generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and signal decomposition techniques are presented and analyzed, including least-square, twin suppo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom :
Academic Press,
2020.
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| Colección: | Wind energy engineering series.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | SCIDIR_on1137796345 | ||
| 003 | OCoLC | ||
| 005 | 20231120010434.0 | ||
| 006 | m o d | ||
| 007 | cr cnu|||unuuu | ||
| 008 | 200129s2020 enkad ob 001 0 eng d | ||
| 040 | |a OPELS |b eng |e rda |e pn |c OPELS |d EBLCP |d OCLCF |d N$T |d OCLCQ |d WAU |d OCLCO |d OCLCQ |d K6U |d OCLCQ |d OCLCO | ||
| 020 | |a 9780128213674 |q (electronic bk.) | ||
| 020 | |a 0128213671 |q (electronic bk.) | ||
| 020 | |z 9780128213537 |q (print) | ||
| 020 | |z 0128213531 |q (print) | ||
| 024 | 7 | |z 10.1016/B978-0-12-821353-7.00012-0 |2 doi | |
| 035 | |a (OCoLC)1137796345 | ||
| 050 | 4 | |a QC931 | |
| 082 | 0 | 4 | |a 551.64180285631 |2 23 |
| 100 | 1 | |a Dhiman, Harsh S., |e author. | |
| 245 | 1 | 0 | |a Supervised machine learning in wind forecasting and ramp event prediction / |c Harsh S. Dhiman, Dipankar Deb and Valentina Emilia Balas. |
| 264 | 1 | |a London, United Kingdom : |b Academic Press, |c 2020. | |
| 300 | |a 1 online resource (xxiii, 191 pages : |b illustrations, charts | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 340 | |g monochrome |2 rdacc | ||
| 340 | |p illustrations | ||
| 340 | |p charts | ||
| 490 | 1 | |a Wind energy engineering series | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | 0 | |t Introduction: |t Renewable energy focus -- |t Wind energy : issues and challenges -- |t Machine learning in allied areas of wind energy -- |t Scope and outline of the book -- |t Wind energy fundamentals: |t Basics of wind power -- |t Wind resource assessment -- |t Wind turbine micrositing -- |t Paradigms in wind forecasting: |t Introduction to time series -- |t Wind forecasting overview -- |t Statistical methods -- |t Machine learning-based models -- |t Hybrid wind forecasting methods -- |t Supervised machine learning models based on support vector regression: |t Support vector regression -- |t ₀-support vector regression -- |t Least-square support vector regression -- |t Twin support vector regression -- |t ₀-twin support vector regression -- |t Decision tree ensemble-based regression models: |t Random forest regression -- |t Gradient boosted machines -- |t Hybrid machine intelligent wind speed forecasting models: |t Introduction -- |t Wavelet transform -- |t Framework of hybrid forecasting -- |t Results and discussion -- |t Empirical mode of decomposition-based SVR variants for wind speed prediction -- |t Ramp prediction in wind farms: |t Ramp events in scientific and engineering activities -- |t Ramp events in wind farms -- |t Ramp event analysis for onshore and offshore wind farms -- |t Supervised learning for forecasting in presence of wind wakes: |t Introduction -- |t Wind wakes -- |t Wake effect in wind forecasting -- |t Results -- |t Epilogue -- |t Introduction to R for machine learning regression: |t Data handling in R -- |t Linear regression analysis in R -- |t Support vector regression in R -- |t Random forest regression in R -- |t Gradient boosted machines in R. |
| 520 | |a "[A]n up to date overview of the broad area of wind generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and signal decomposition techniques are presented and analyzed, including least-square, twin support, and random forest regression, all with supervised Machine Learning. The specific topics of ramp event prediction and wake interactions are addressed in this book along with forecasted performance, with the authors providing a variety of wind farm datasets and conducted statistical tests to ascertain the robustness of the presented prediction models. Wind speed forecasting has become an essential component to ensure power system security, reliability, and safe operation, making this reference useful for all researchers and professionals researching in renewable energy and wind energy forecasting and generation"--Page 4 of cover | ||
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Wind forecasting |x Data processing. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Vents |0 (CaQQLa)201-0102604 |x Pr�evision |0 (CaQQLa)201-0102604 |x Informatique. |0 (CaQQLa)201-0380011 | |
| 650 | 6 | |a Apprentissage automatique. |0 (CaQQLa)201-0131435 | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 700 | 1 | |a Deb, Dipankar, |e author. | |
| 700 | 1 | |a Balas, Valentina Emilia, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Dhiman, Harsh S. |t Supervised machine learning in wind forecasting and ramp event prediction. |d London, United Kingdom : Academic Press, 2020 |z 9780128213537 |w (DLC) 2020300720 |w (OCoLC)1120689105 |
| 830 | 0 | |a Wind energy engineering series. | |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128213537 |z Texto completo |