Forecasting electricity prices: The impact of fundamentals and time-varying coefficients

By Nektaria V. Karakatsani, D. W. Bunn. 2008

251 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cda259fc322c85577d336fd4271737feee26e222

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In Electrical and Electronic Engineering

Citations in the corpus

Modeling and forecasting electricity spot prices: A functional data perspective (2013, methodology): “1562 (2006) and Koopman, Ooms and Carnero (2007)] or by considering each hour h separately [Karakatsani and Bunn (2008)].”
Efficient modeling and forecasting of electricity spot prices (2014, unknown): “in Kristiansen (2012)) Nevertheless, the consideration of special day or special phases of days is done only rarely.”
Electricity prices, large-scale renewable integration, and policy implications (2017, background): “…of consumers to alter their consumption patterns in the short-run, while electricity supply or merit-order curve is discontinuous, convex, and sharply increasing at the high demand level (Karakatsani and Bunn, 2008), indicating the special characteristics of the electricity power generation mix.”
Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices (2013, background): “Karakatsani and Bunn (2008) add fundamental variables such as fuel prices and level of demand, although their focus is mostly on accurately modeling the volatility of electricity prices.”
On the market impact of wind energy forecasts (2010, background): “Therefore, those factors ought to be supplemented by, or transformed into, information that more directly affects the supply function and the behaviour of market participants in general, as suggested by Karakatsani and Bunn (2008) and Longstaff and Wang (2004).”
Forecasting Electricity Spot Prices Accounting for Wind Power Predictions (2013, background): “Meanwhile, the supply function is discontinuous, convex, and steeply increasing at the high production end [9], [13], [14].”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, methodology): “…regime(s), on the other hand, a number of different specifications have been suggested in the literature, ranging from mean-reverting diffusions (Karakatsani & Bunn, 2008), to Gaussian (Huisman & de Jong, 2003; Liebl, 2013), lognormal (Weron, Bierbrauer et al., 2004), exponential (Bierbrauer,…”
Variance Stabilizing Transformations for Electricity Spot Price Forecasting (2018, unknown): “Firstly, the existing literature on short-term EPF which has generally favored the multivariate framework, with prices for each hour of the day modeled independently by 24 parsimonious models rather than jointly by one large model [1], [27].”
Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, methodology): “In the first two autoregressive structures (ARX and mARX) the modeling is implemented separately across the hours, leading to 24 sets of parameters for each day, an approach commonly taken in EPF studies [29, 35, 42, 43, 72, 117, 118].”
Electricity price forecasting using sale and purchase curves: The X-Model (2015, methodology): “First, there are regime-switching models, which introduce diﬀerent regimes, usually a base and a spike regime, with diﬀerent probabilities for a price spike to occur (see, for instance Karakatsani and Bunn (2008), Janczura and Weron (2012), Eichler and Tuerk (2013)).”
Forecasting Electricity Spot Prices Using Lasso: On Capturing the Autoregressive Intraday Structure (2015, background): “For example Karakatsani and Bunn (2008a) propose a model with a linear price dependency on a daily lag of order 1 and 7, but not at lag 2.”