TIME SERIES ANALYSIS USING VECTOR AUTO REGRESSIVE (VAR) MODEL OF WIND SPEEDS IN BANGUI BAY AND SELECTED WEATHER VARIABLES IN LAOAG CITY, PHILIPPINES

Abstract

Wind energy is the fastest growing renewable energy technology. Wind turbines do not produce any form of pollution and when strategically placed, it naturally blends with the natural landscape. In the long run, the cost of electricity using wind turbines is cheaper than conventional power plants since it doesn’t consume fossil fuel. Wind speed modelling and forecasting are important in the wind energy industry starting from the feasibility stage to actual operation. Forecasting wind speed is vital in the decision-making process related to wind turbine sizes, revenues, maintenance scheduling and actual operational control systems. This paper models and forecasts wind speeds of turbines in the Northwind Bangui Bay wind farm using the Vector Auto Regressive (VAR) model. The explanatory variables used are local wind speed (Laoag), humidity, temperature and pressure generated from the meteorological station in Laoag City. Wind speeds of turbines and other weather factors were found to be stationary using Augmented Dickey-Fuller (ADF) test. The use of VAR model, from daily time series data, reveals that wind speeds of the turbines can be explained by the past wind speed, the wind speed in Laoag, humidity, temperature and pressure. Results of the analysis, using the forecast error variance decomposition, show that wind speed in Laoag, temperature and humidity are important determinants of the wind speeds of the turbines.