Forecasting electricity demand using generalized long memory
This paper studies the electricity hourly load demand in the area covered by a utility situated in the southeast of Brazil. We propose a stochastic model which employs generalized long memory (by means of Gegenbauer processes) to model the seasonal behavior of the load. The model is proposed for sectional data, that is, each hour’s load is studied separately as a single series. This approach avoids modeling the intricate intra-day pattern (load profile) displayed by the load, which varies throughout days of the week and seasons. The forecasting performance of the model is compared with a SARIMA benchmark using the years of 1999 and 2000 as the out-of-sample. The model clearly outperforms the benchmark. We conclude for general long memory in the series.
(This abstract was borrowed from another version of this item.)
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.:
- Josu Arteche & Peter M. Robinson, 1998. "Seasonal and cyclical long memory," LSE Research Online Documents on Economics 2241, London School of Economics and Political Science, LSE Library.
- Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
- repec:crs:wpaper:9927 is not listed on IDEAS
- L. A. Gil-Alana & P. M. Robinson, 2001.
"Testing of seasonal fractional integration in UK and Japanese consumption and income,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 16(2), pages 95-114.
- Gil-Alana, L. & Robinson, P.M., 1998. "Testing of Seasonal Fractional Integration in U.K. and Japanese Consumption and Income," Economics Working Papers eco98/20, European University Institute.
- L A Gil-Alana & Peter M. Robinson, 2000. "Testing of seasonal fractional integration in UK and Japanese consumption and income," LSE Research Online Documents on Economics 2051, London School of Economics and Political Science, LSE Library.
- Gil-Alana, Luis A., 2002. "Seasonal long memory in the aggregate output," Economics Letters, Elsevier, vol. 74(3), pages 333-337, February.
- Ferrara, Laurent & Guegan, Dominique, 2001. "Forecasting with k-Factor Gegenbauer Processes: Theory and Applications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(8), pages 581-601, December.
- Laurent Ferrara & Dominique Guegan, 2001. "Forecasting with k-factor Gegenbauer Processes: Theory and Applications," Post-Print halshs-00193667, HAL.
- Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
- Ray, Bonnie K., 1993. "Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model," International Journal of Forecasting, Elsevier, vol. 9(2), pages 255-269, August.
- Laurent Ferrara & Dominique Guegan, 1999. "Estimation and Applications of Gegenbauer Processes," Working Papers 99-27, Centre de Recherche en Economie et Statistique.
- Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
- Chung, Ching-Fan, 1996. "Estimating a generalized long memory process," Journal of Econometrics, Elsevier, vol. 73(1), pages 237-259, July.
- Lacir J. Soares & Marcelo Cunha Medeiros, 2005. "Modelling and forecasting short-term electricity load: a two step methodology," Textos para discussão 495, Department of Economics PUC-Rio (Brazil).
- L. A. Gil-Alaña & Peter M. Robinson, 2001. "Testing of seasonal fractional integration in UK and Japanese consumption and income," LSE Research Online Documents on Economics 298, London School of Economics and Political Science, LSE Library.
- Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:22:y:2006:i:1:p:17-28. 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: (Dana Niculescu)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.