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Forecasting electricity prices with expert, linear, and nonlinear models

Anna Gloria Billé, Angelica Gianfreda, Filippo Del Grosso and Francesco Ravazzolo

International Journal of Forecasting, 2023, vol. 39, issue 2, 570-586

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.

Keywords: Demand; Wind; Solar; Biomass; Waste; Fossil fuels (coal, natural gas, CO2); Weighted inflows; Commercial and public forecasts (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Forecasting Electricity Prices with Expert, Linear and Non-Linear Models (2021) Downloads
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DOI: 10.1016/j.ijforecast.2022.01.003

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Handle: RePEc:eee:intfor:v:39:y:2023:i:2:p:570-586