Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction (notice n° 1330812)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01975cam a2200301zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88965782 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250429184046.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250429s2020 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780128213537 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88965782 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Dhiman, Harsh S. |
| 245 01 - TITLE STATEMENT | |
| Title | Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction |
| Statement of responsibility, etc. | ['Dhiman, Harsh S.', 'Deb, Dipankar', 'Emilia Balas Phd, Valentina'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | Academic Press |
| Date of production, publication, distribution, manufacture, or copyright notice | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction provides an up-to- date overview on 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. 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 renewable energy, wind energy forecasting and generation. - Features various supervised machine learning based regression models - Offers global case studies for turbine wind farm layouts - Includes state-of-the-art models and methodologies in wind forecasting |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Dhiman, Harsh S. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Deb, Dipankar |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Emilia Balas Phd, Valentina |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88965782">https://international.scholarvox.com/netsen/book/88965782</a> |
| Electronic format type | text/html |
| Host name | |
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