A time series-based approach for renewable energy modeling
Despite the growing literature on renewable energy sources, causal relationships between the variables that are selected as inputs of the models proposed in forecasting studies have not been investigated so far. In this paper, a novel approach to decide prediction input variables of wind and/or temperature forecasting models is suggested. This approach uses time series techniques; more specifically, Granger causality and impulse-response analyses between some meteorological variables. To conduct our study, wind speed, temperature and pressure data obtained from different regions of Turkey are employed. The results suggest that bidirectional causal relationships exist between these variables and that short-run dynamics differ with respect to location (inland versus coastal area). From this, it is concluded that renewable energy models must be built accordingly to improve prediction accuracy.
Volume (Year): 28 (2013)
Issue (Month): C ()
|Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description|
|Order Information:|| Postal: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
- M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
- Pesaran, M. H. & Shin, Y., 1997.
"Generalised Impulse Response Analysis in Linear Multivariate Models,"
Cambridge Working Papers in Economics
9710, Faculty of Economics, University of Cambridge.
- Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
- Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
- Cadenas, Erasmo & Rivera, Wilfrido, 2010. "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model," Renewable Energy, Elsevier, vol. 35(12), pages 2732-2738.
- Haque, Ashraf U. & Mandal, Paras & Kaye, Mary E. & Meng, Julian & Chang, Liuchen & Senjyu, Tomonobu, 2012. "A new strategy for predicting short-term wind speed using soft computing models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4563-4573.
- Lutkepohl, Helmut, 1982. "Non-causality due to omitted variables," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 367-378, August.
- Karanfil, Fatih, 2008. "Energy consumption and economic growth revisited: Does the size of unrecorded economy matter?," Energy Policy, Elsevier, vol. 36(8), pages 3019-3025, August.
- Karanfil, Fatih, 2009. "How many times again will we examine the energy-income nexus using a limited range of traditional econometric tools?," Energy Policy, Elsevier, vol. 37(4), pages 1191-1194, April.
- Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
- Mellit, A. & Kalogirou, S.A. & Hontoria, L. & Shaari, S., 2009. "Artificial intelligence techniques for sizing photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 406-419, February.
- Soares, Jacyra & Oliveira, Amauri P. & Boznar, Marija Zlata & Mlakar, Primoz & Escobedo, João F. & Machado, Antonio J., 2004. "Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique," Applied Energy, Elsevier, vol. 79(2), pages 201-214, October.
- Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211.
- Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
- Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2006. "An adaptive wavelet-network model for forecasting daily total solar-radiation," Applied Energy, Elsevier, vol. 83(7), pages 705-722, July.
- Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
- Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
- Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
- Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:28:y:2013:i:c:p:204-214. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If references are entirely missing, you can add them using this form.