Forecasting electricity prices for a day-ahead pool-based electric energy market

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Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power (2007, methodology): “In such a case, regulation prices cannot be forecast prior to defining an optimal bid, unless using in a first stage a model for forecasting spot prices [ 22 ].”
Short-Term Trading for a Wind Power Producer (2010, methodology): “Seasonal autoregressive integrated moving average (ARIMA) models [17] have been used to characterize the following.”
On the importance of the long-term seasonal component in day-ahead electricity price forecasting (2016, methodology): “As was argued by Conejo et al. (2005) and Nogales et al.”
A Bilevel Stochastic Programming Approach for Retailer Futures Market Trading (2009, methodology): “Time series models [14], [15] are used to generate different realizations or scenarios of the random variables.”
Real-Time Demand Response Model (2010, methodology): “Conventional forecasting techniques are used to compute such confidence interval [5], [6].”
Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities (2013, background): “at = σtεt, εt ∼ NIID(0, 1), σ(2)t = ω + αa 2 t−1, (9)”