This paper compares several models for forecasting regional hourly day-ahead electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources, fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA-GARCH models—hence including parsimonious autoregressive specifications (known as expert-type models). The results support the adoption of a simple structure that is able to adapt to market conditions. Indeed, we include forecasted demand, wind and solar power, actual generation from hydro, biomass, and waste, weighted imports, and traditional fossil fuels. The inclusion of these exogenous regressors, in both the conditional mean and variance equations, outperforms in point and, especially, in density forecasting when the superior set of models is considered. Indeed, using the model confidence set and considering northern Italian prices, predictions indicate the strong predictive power of regressors, in particular in an expert model augmented for GARCH-type time-varying volatility. Finally, we find that using professional and more timely predictions of consumption and renewable energy sources improves the forecast accuracy of electricity prices more than using predictions publicly available to researchers.

Forecasting electricity prices with expert, linear, and nonlinear models / Anna Gloria Billé; Angelica Gianfreda; Filippo Del Grosso; Francesco Ravazzolo. - In: INTERNATIONAL JOURNAL OF FORECASTING. - ISSN 0169-2070. - STAMPA. - 39:2 (April-June)(2023), pp. 570-586. [10.1016/j.ijforecast.2022.01.003]

Forecasting electricity prices with expert, linear, and nonlinear models

Anna Gloria Billé;Angelica Gianfreda
;
2023

Abstract

This paper compares several models for forecasting regional hourly day-ahead electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources, fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA-GARCH models—hence including parsimonious autoregressive specifications (known as expert-type models). The results support the adoption of a simple structure that is able to adapt to market conditions. Indeed, we include forecasted demand, wind and solar power, actual generation from hydro, biomass, and waste, weighted imports, and traditional fossil fuels. The inclusion of these exogenous regressors, in both the conditional mean and variance equations, outperforms in point and, especially, in density forecasting when the superior set of models is considered. Indeed, using the model confidence set and considering northern Italian prices, predictions indicate the strong predictive power of regressors, in particular in an expert model augmented for GARCH-type time-varying volatility. Finally, we find that using professional and more timely predictions of consumption and renewable energy sources improves the forecast accuracy of electricity prices more than using predictions publicly available to researchers.
2023
Forecasting electricity prices with expert, linear, and nonlinear models / Anna Gloria Billé; Angelica Gianfreda; Filippo Del Grosso; Francesco Ravazzolo. - In: INTERNATIONAL JOURNAL OF FORECASTING. - ISSN 0169-2070. - STAMPA. - 39:2 (April-June)(2023), pp. 570-586. [10.1016/j.ijforecast.2022.01.003]
Anna Gloria Billé; Angelica Gianfreda; Filippo Del Grosso; Francesco Ravazzolo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/897303
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