Forecasting spikes in electricity prices

By Timothy M. Christensen, S. Hurn, Kenneth P. Lindsay. 2012

125 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/9a7887d9ca07e80f340796df3db0fb9ef67af27f

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In Economics and Econometrics

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Citations in the corpus

Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices (2013, methodology): “Christensen et al. (2012) focus on predicting the occasional extreme spikes in electricity prices using an autoregressive conditional hazard model.”
Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, background): “[64] treat the time series of spikes as a discrete-time point process and represent it as a nonlinear variant of the autoregressive conditional hazard (ACH) model.”
Efficient modeling and forecasting of electricity spot prices (2014, unknown): “According to Christensen et al. (2012) there are mainly three diﬀerent common approaches.”
Electricity price forecasting using sale and purchase curves: The X-Model (2015, background): “Rarely there are approaches which focus solely on the price spike itself and try to forecast the event without modeling the whole price time series, e.g. in Christensen et al. (2012).”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, methodology): “With a similar goal in mind, Chan, Gray, and van Campen (2008) explore a recently developed method of separating the total variation into jump and non-jump components, and, using quadratic variation theory, non-parametrically estimate jump parameters for five zones of the Australian power market.”
Modeling and forecasting multivariate electricity price spikes (2016, methodology): “…of the variables and the precise nesting structure we use the approach suggested 5Here we use the actual loads instead of predictions as argued by Christensen et al. (2012) because half-hourly load forecasts are typically very precise and the authors show that using true instead of predicted…”