Integrating long-term economic scenarios into peak load forecasting: An application to Spain
Moral-Carcedo, Julián and
Authors registered in the RePEc Author Service: Julian Moral Carcedo () and
Julian Perez Garcia ()
Energy, 2017, vol. 140, issue P1, 682-695
The treatment of trend components in electricity demand is critical for long-term peak load forecasting. When forecasting high frequency variables, like daily or hourly loads, a typical problem is how to make long-term scenarios - regarding demographics, GDP growth, etc. - compatible with short-term projections. Traditional procedures that apply de-trending methods are unable to simulate forecasts under alternative long-term scenarios. On the other hand, existing models that allow for changes in long-term trends tend to be characterized by end-of-year discontinuities. In this paper a novel forecasting procedure is presented that improves upon these approaches and is able to combine long and short-term features by employing temporal disaggregation techniques. This method is applied to forecast electricity load for Spain and its performance is compared to that of a nonlinear autoregressive neural network with exogenous inputs. Our proposed procedure is flexible enough to be applied to different scenarios based on alternative assumptions regarding both long-term trends as well as short-term projections.
Keywords: Peak load forecasting; Load curve forecasting; Long-term scenarios; Temporal disaggregation (search for similar items in EconPapers)
JEL-codes: Q4 L94 C53 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:140:y:2017:i:p1:p:682-695
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