Time Series Analysis using Vector Autoregressive 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. Moreover, when strategically positioned the wind turbines blend with the area’s natural landscape. In the long run, the cost of electricity using wind turbines is cheaper than conventional power plants since it does not consume fossil fuel. Wind speed modeling 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 uses econometric models to forecast the wind speeds of turbines in the Northwind Bangui Bay wind farm located in the Province of IlocosNorte, Philippines,using the Vector Auto Regressive (VAR) model. The explanatory variables used are local wind speed, humidity, temperature and pressure generated from the meteorological station in Laoag City, Province of IlocosNorte, Philippines. The use of VAR model, using daily time series data, reveals that wind speeds of the turbines can be explained by the past wind speed, the wind speed in Laoag City, humidity, temperature and pressure. Results of the analysis, using the forecast error variance decomposition, show that wind speed in Laoag City, temperature and humidity are important determinants of the wind speeds of the turbines.