Probabilistic forecasting of electricity prices using an augmented LMARX-model

Jonas Andersson and Samaneh Sheybanivaziri (samaneh.sheybanivaziri@nhh.no)
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Samaneh Sheybanivaziri: Dept. of Business and Management Science, Norwegian School of Economics, Postal: NHH , Department of Business and Management Science, Helleveien 30, N-5045 Bergen, Norway, https://www.nhh.no/en/employees/faculty/samaneh-sheybanivaziri/

No 2023/11, Discussion Papers from Norwegian School of Economics, Department of Business and Management Science

Abstract: In this paper, we study the performance of prediction intervals in situations applicable to electricity markets. In order to do so we first introduce an extension of the logistic mixture autoregressive with exogenous variables (LMARX) model, see (Wong, Li, 2001), where we allow for multiplicative seasonality and lagged mixture probabilities. The reason for using this model is the prevalence of spikes in electricity prices. This feature creates a quickly varying, and sometimes bimodal, forecast distribution. The model is fitted to the price data from the electricity market forecasting competition GEFCom2014. Additionally, we compare the outcomes of our presumably more accurate representation of reality, the LMARX model, with other widely utilized approaches that have been employed in the literature.

Keywords: Prediction intervals; probabilistic forecasts; electricity prices; spikes; mixture models (search for similar items in EconPapers)
JEL-codes: C10 C50 C53 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2023-07-11
New Economics Papers: this item is included in nep-ecm, nep-ene and nep-for
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