Forecasting electricity demand using generalized long memory

Lacir Jorge Soares and Leonardo Souza

No 486, FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) from EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil)

Abstract: 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.

Date: 2003-06-29
New Economics Papers: this item is included in nep-ecm
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Journal Article: Forecasting electricity demand using generalized long memory (2006)
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