WIND SPEED FORECASTINGUSING IN THE PRESENCE OF SOME METEOROLOGICAL VARIABLES USING VECTOR AUTOREGRESSIVE AND MULTINOMIAL LOGISTIC REGRESSION MODEL

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dc.contributor.authorODEKINA, Gabriel Onuche
dc.date.accessioned2018-10-09T14:37:50Z
dc.date.available2018-10-09T14:37:50Z
dc.date.issued2017-12
dc.descriptionA DISSERTATION SUBMITTED TO THE SCHOOL OF POSTGRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER DEGREE IN STATISTICS DEPARTMENT OF STATISTICS, FACULTY OF PHYSICAL SCIENCE, AHMADU BELLO UNIVERSITY, ZARIA, NIGERIAen_US
dc.description.abstractWind speed forecasting is pertinent in order to have a power system that is reliable and secured. The Vector Autoregressive model and the Multinomial logistic regression model were applied on a 30 year daily data obtained from the department of Soil science, Ahmadu Bello University, Zaria. The Vector Autoregressive model shows that non of the meteorological variables namely; rainfall, air temperature, earth temperature, sunshine, humidity, pressure and evaporation significantly affect wind speed. It was also found that each of the meteorological variables have varying effects on Wind speed over a future time horizon as depicted by the impulse response function. The Wind direction was also examined using the Multinomial logistic regression where it was found that there is no Statistical significant difference between Wind speed and the Northern and Western direction. To this end, this study posits that wind speed is not significantly being influenced by the meteorological variables and the northern and Western directions do not exert any effect on Wind speed.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/10582
dc.language.isoenen_US
dc.subjectWIND SPEED,en_US
dc.subjectFORECASTINGUSING,en_US
dc.subjectMETEOROLOGICAL VARIABLESen_US
dc.subjectVECTOR AUTOREGRESSIVE,en_US
dc.subjectMULTINOMIAL LOGISTIC,en_US
dc.subjectREGRESSION MODEL,en_US
dc.titleWIND SPEED FORECASTINGUSING IN THE PRESENCE OF SOME METEOROLOGICAL VARIABLES USING VECTOR AUTOREGRESSIVE AND MULTINOMIAL LOGISTIC REGRESSION MODELen_US
dc.typeThesisen_US
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