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.References
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[4] Enders. W. (2004). Applied Econometric Time Series, 2nd edition. John Wiley.
[5] Ewing, Bradley T, Kruse, Jamie Brown, Schroeder, John L. and Smith, Douglas, A. (2006). Time Series Analysis of Windspeed Using VAR and the Generalized Impulse Response Technique. Revised March 2006. Retrieved at http://www.ecu.edu/cs-educ/econ/upload/ecu0607.pdf (March 30, 2012)
[6] Hannan, E.J. and G.G. Quin (1979). The determination of the order of an AutoRegression. Journal of the Royal Statistical Society B, 41: 190-195.
[7] Hu, M. (2002). Wind Speed Analysis for Lake Okeechobee. Unpublished thesis. Florida Atlantic University.
[8] Jones, R.H. (1980). Maximum Likelihood Fitting of ARM Models to Time Series with missing Observations, Technometrics, 22(3):389-395.
[9] Lutkepohl, H. (1991). Introduction to Multiple Time Series.Springer, New York.
[10] Mapa, Dennis S., Fatima C. Han and Kristine Claire O. Estrada (2011). Food Inflation, Underemployment and Hunger Incidence: A Vector Autoregressive (VAR) Analysis, The Philippine Statistician, 60: 43 – 62.
[11] Marvel, K., Kravitz, B., Caldeira, K. (2012). Geophysical limits to global wind power, Nature Climate Change, 3: 118–121.
[12] Mills,T. (1999). The Econometric Modelling for Financial Time Series. 2nd Ed. Cambridge University Press. Cambridge.
[13] Schafer, J.L. (1999). Analysis of Incomplete Multivariate Data.Chapman & Hall, Boca Raton.
[14] Tastu, J., Pinson, P., Kotwa, E., Madsen, H. (2010). Spatio-temporal and modelling of short term wind power forecast errors, Wind Energy.
[15] Webb, A. (1999). Statistical Pattern Recognition. New York, Oxford University Press.
[16] Yao, Y., Huang, G.H., Lin, Q. (2012). Climate Change impacts on Ontario Wind Power Resource. Environmental Systems Research, DOI:10.1186/2193-2697-1-2
Published
2016-11-14
How to Cite
ORPIA, Cherie B.; MAPA, Dennis S.; ORPIA, Julius C..
Time Series Analysis using Vector Autoregressive Model of Wind Speeds in Bangui Bay and Selected Weather Variables in Laoag City, Philippines.
Journal of Environmental Management and Tourism, [S.l.], v. 5, n. 1, p. 52-62, nov. 2016.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/396>. Date accessed: 18 apr. 2024.
Section
Journal of Environmental Management and Tourism
Keywords
wind speed; Vector Auto Regressive (VAR) model; impulse response function; variance decomposition
Copyright© 2023 The Author(s). Published by ASERS Publishing 2023. This is an open access article distributed under the terms of CC-BY 4.0 license.