Forecasting Electricity Spot Prices Accounting for Wind Power Predictions

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Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, methodology): “…error (RMSPE; see Hyndman & Koehler, 2006), or by the square of the mean daily (or weekly) price to yield the daily- or weeklyweighted root mean square errors (DRMSE, WRMSE), or by 1 T−24 T h=25 (Ph − Ph−24) 2 to yield the (seasonal) root mean square scaled error (RMSSE; see Jonsson et al., 2013).”
Forecasting Functional Time Series with a New Hilbertian ARMAX Model: Application to Electricity Price Forecasting (2018, background): “In addition, electricity demand and wind power production are relevant variables to forecast prices as shown in [38] and [39].”
A bilevel model for electricity retailers' participation in a demand response market environment (2013, methodology): “We consider a constant standard deviation σ=€ 6.67, which is the approximate RMSE value for the spot market price forecasting model in the work in Jónsson et al. (2013).1 Furthermore we point out that the time-lags considered for these scenarios are at least 13 h, which is the look-ahead time of…”