Combining day-ahead forecasts for British electricity prices

By Silvano Bordignon, D. W. Bunn, Francesco Lisi, F. Nan. 2013

144 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b53a5f6ea4980a3683ac41673ba895e6e3f86b0e

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Citations in the corpus

Forecasting: theory and practice (2020, methodology): “For instance, Bosq (2000) and Bosq and Blanke (2007) proposed the functional autoregressive of order 1 (FAR(1)) and derived one-step-ahead forecasts that are based on a regularised Yule-Walker equations. FAR(1) was later extended to FAR(p), under which the order p can be determined via Kokoszka and ”
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark (2020, background): “The univariate version of the test has the advantage of providing a deeper analysis as it indicates which forecast is significantly better for which hour of the day [6, 55, 59, 66, 120, 121].”
An empirical comparison of alternate schemes for combining electricity spot price forecasts (2013, unknown): “To our best knowledge, existing applications so far only include the work by Bordignon et al. (2013), Løland et al. (2012), Raviv et al. (2013), Smith (1989), Taylor (2010) and Taylor and Majithia (2000). Hereby, earlier studies concentrate on forecasting electricity demand or transmission congestio”
Efficient modeling and forecasting of electricity spot prices (2014, unknown): “Other authors take advantage of a combination of these models, e.g. Bordignon et al. (2013) and Escribano et al. (2011).”
On the importance of the long-term seasonal component in day-ahead electricity price forecasting (2016, methodology): “Finally, an added value of our SCAR methodology is that it creates a natural opportunity for considering forecast combinations, an approach which has been shown to provide additional accuracy gains in day-ahead forecasting of electricity spot prices (Bordignon et al., 2013; Nowotarski et al., 2014).”
Forecasting Electricity Spot Prices Using Lasso: On Capturing the Autoregressive Intraday Structure (2015, background): “Conejo et al. (2005), Singhal and Swarup (2011), Catalao et al.”
The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts (2018, background): “(2014) and Bordignon et al. (2013) and aims at investigating the outperformance of”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, methodology): “thesis of Nan (2009), it was only very recently that Bordignon et al. (2013); Maciejowska et al. (2014); Nowotarski et al. (2014); Nowotarski and Weron (2014a,b) and Raviv et al. (2013) provided empirical support for the benefits of combining forecasts in obtaining better predictions of electricity ”
Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, methodology): “, each hour of the day) separately [42, 71, 112, 117].”
Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging (2016, background): “Indeed, as Bordignon et al. (2013), Nowotarski et al. (2013) and Raviv et al. (2013) report, combining does lead to more accurate (on average) and more robust electricity price point forecasts.”