2020/09/16 by Angus Lewis, Lewis, Angus, Nigel Bean +3
Economics, Econometrics and Finance · Engineering · Mathematics · #Artificial intelligence #Bayesian inference #Bayesian probability #Computer science #Econometrics #Economics #Electric Power System Optimization #Electricity #Electricity market #Energy Load and Power Forecasting #Engineering #Estimation #FOS: Computer and information sciences #Inference #Markov chain #Markov process #Mathematics #Methodology (stat.ME) #Model selection #Monetary Policy and Economic Impact #Selection (genetic algorithm) #Statistics
paper · pdf · doi:10.48550/arxiv.2009.07471
openalex publication_date 2020/09/16 · openalex created_date 2020/09/21 · openalex updated_date 2026/07/28
We discuss and extend methods for estimating Markovian-Regime-Switching (MRS) and trend models for wholesale electricity prices. We argue the existing methods of trend estimation used in the electricity price modelling literature either require an ambiguous definition of an extreme price, or lead to issues when implementing model selection [23]. The first main contribution of this paper is to design and infer a model which has a model-based definition of extreme prices and permits the use of model selection criteria. Due to the complexity of the MRS models inference is not straightforward. In the existing literature an approximate EM algorithm is used [26]. Another contribution of this paper is to implement exact inference in a Bayesian setting. This also allows the use of posterior predictive checks to assess model fit. We demonstrate the methodologies with South Australian electricity market.