000			01975cam a2200301zu 4500
001			88965782
003			FRCYB88965782
005			20250429184046.0
006			m o d
007			cr un
008			250429s2020 fr | o|||||0|0|||eng d
020			_a9780128213537
035			_aFRCYB88965782
040			_aFR-PaCSA _ben _c _erda
100	1		_aDhiman, Harsh S.
245	0	1	_aSupervised Machine Learning in Wind Forecasting and Ramp Event Prediction _c['Dhiman, Harsh S.', 'Deb, Dipankar', 'Emilia Balas Phd, Valentina']
264		1	_bAcademic Press _c2020
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aDhiman, Harsh S.
700	0		_aDeb, Dipankar
700	0		_aEmilia Balas Phd, Valentina
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88965782 _qtext/html _a
520			_aSupervised 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
999			_c1330812 _d1330812