Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

By Rafal Weron, Adam Misiorek. 2008

388 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e91eec5c945d0fc669eab7c58668193eaad39302

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Citations in the corpus

Variance Stabilizing Transformations for Electricity Spot Price Forecasting (2018, methodology): “measure used in [10], [38] for comparing models.”
On the importance of the long-term seasonal component in day-ahead electricity price forecasting (2016, methodology): “(2006) and later used in a number of EPF studies (Gaillard et al., 2016; Kristiansen, 2012; Maciejowska et al., 2016; Nowotarski and Weron, 2016; Nowotarski et al., 2014; Serinaldi, 2011; Uniejewski et al., 2016; Weron, 2006; Weron and Misiorek, 2008; Ziel, 2016).”
Electricity price forecasting using sale and purchase curves: The X-Model (2015, methodology): “For example, Weron and Misiorek (2008) allow for a linear dependency of X price ,d,h to X price ,d − 1 ,h , X price ,d − 2 ,h and X price ,d − 7 ,h as well as dummies on Sunday, Monday and Saturday.”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, background): “Alternative normalizations have been proposed instead, see for exampleMisiorek, Trück, andWeron (2006);Nogales andConejo (2006); Shahidehpour et al. (2002); Weron and Misiorek (2008), and the references in the paragraphs below.”
Time series forecasting for nonlinear and non-stationary processes: a review and comparative study (2015, background): “Weron and Misiorek (2008) reviewed AR models and their extensions for short-term time series forecasting.”
Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging (2016, methodology): “Hereby, two sets of lags are considered: either all lags from 1 to 7 are included (as in Maciejowska and Weron, 2013) or only lags 1, 2 and 7 are used (as in Weron and Misiorek, 2008).”
Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape (2011, background): “From 2006, only few authors have complemented the stochastic point forecasts with confidence intervals (e.g., Misiorek and Weron, 2006; Misiorek et al., 2006; Nogales and Conejo, 2006; Weron and Misiorek, 2008).”
An empirical comparison of alternate schemes for combining electricity spot price forecasts (2013, neutral): “Weeks 1-5 correspond to period II (February) in Weron and Misiorek (2008), weeks 13-17 to period V (May), weeks 27-31 to period VIII (August) and weeks 40-44 to period XI (November).”
Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, methodology): “A number of methods can be used for this purpose, the most popular take into account both the point forecast and the corresponding error [24, 29]: the center of the PI at the (1 − α) confidence level is set equal to P̂t and its bounds are defined by the α2 th and (1 − α 2 )th quantiles of the cumula”
Modeling of financial incentives for investments in energy storage systems that promote the large-scale integration of wind energy (2013, background): “In particular, as many authors have recently pointed out [27–29], adoption of arbitrage strategies based on spot price spreads is not in itself sufficient to ensure cost-effectiveness, with imperfect long-term prognosis of the spot price [30] implying an extremely high-risk investment.”
An Adaptive Functional Autoregressive Forecast Model to Predict Electricity Price Curves (2017, methodology): “The data were studied by Weron and Misiorek (2008).”
Forecasting Electricity Spot Prices Using Lasso: On Capturing the Autoregressive Intraday Structure (2015, methodology): “, [14] or [15]).”