A Regime Switching Long Memory Model for Electricity Prices

Day-ahead electricity price prediction based on multiple ELM

“…Figure 1 shows the scheme of multi-ELM modeling method. 1 At the beginning of training of whole multi-ELM, the initial values are 1…”

“…The main methods of electricity price prediction could be divided into tow classes. One class is traditional mechanism method which computes the electricity price by modeling an electricity power market model of real operation [1][2][3] . This kind of methods can reflect the real states of electricity power market and have a good performance of electricity price prediction in short-term.…”

A novel prediction modeling scheme based on multiple information fusion for day-ahead electricity price

“…One class is traditional mechanism method which computes the electricity price by modeling an electricity power market model of real operation [1][2][3]. This kind of methods can reflect the real states of electricity power market and have a good performance of electricity price prediction in short-term.…”

The Forecasting Performance of a Finite Mixture Regime‐Switching Model for Daily Electricity Prices

“…Intra-day power prices have a fine structure and are driven by an interaction of fundamental, behavioural and stochastic factors. Moreover, the dynamic analysis of model specifications appears to suggest both evolutionary and abrupt changes in the impact of exogenous drivers (Chen and Bunn, 2010;Gonzalez et al, 2012) and, in particular, substantial changes in fuel and carbon allowance prices as well as water flows, solar and wind production can, for example, change the profile of the price-setting power plants.Research on the dynamics of intra-day price formation is therefore strongly persuasive of the benefits of nonlinear models and, while the dominant suggestions are towards the general class of regime-switching methods, it remains as an open question, whether the apparently better in-sample fits and explanatory power, ex post, of nonlinear, multi factor methods, translates into more accurate, robust ex ante price forecasting (contrast the positive evidence for regime switching in Gonzalez et al, 2005Gonzalez et al, , 2012Bierbrauer et al, 2007;Bunn, 2008b, andChen andBunn, 2010, with the results in favour of forecasting with linear models in Haldrup and Nielsen, 2006;Kosater andMosler, 2006), andMisiorek et al, 2006). Markov regime switching has been widely advocated to capture some aspects of the nonlinearity, but it may suffer from overfitting and unobservability in the underlying states.…”

“…Research on the dynamics of intra-day price formation is therefore strongly persuasive of the benefits of nonlinear models and, while the dominant suggestions are towards the general class of regime-switching methods, it remains as an open question, whether the apparently better in-sample fits and explanatory power, ex post, of nonlinear, multi factor methods, translates into more accurate, robust ex ante price forecasting (contrast the positive evidence for regime switching in Gonzalez et al, 2005Gonzalez et al, , 2012Bierbrauer et al, 2007;Bunn, 2008b, andChen andBunn, 2010, with the results in favour of forecasting with linear models in Haldrup and Nielsen, 2006;Kosater andMosler, 2006), andMisiorek et al, 2006). Dacco and Satchell (1999) suggest that state estimation in regime-switching models is a delicate process and that slight misclassifications can undermine their average performance compared to linear models.…”