Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain

Stephen Haben, Julien Caudron and Jake Verma
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Stephen Haben: Energy Systems Catapult, Cannon House, Birmingham B4 6BS, UK
Julien Caudron: Energy Systems Catapult, Cannon House, Birmingham B4 6BS, UK
Jake Verma: Energy Systems Catapult, Cannon House, Birmingham B4 6BS, UK

Forecasting, 2021, vol. 3, issue 3, 1-37

Abstract: The energy sector is moving towards a low-carbon, decentralised, and smarter network. The increased uptake of distributed renewable energy and cheaper storage devices provide opportunities for new local energy markets. These local energy markets will require probabilistic price forecasting models to better describe the future price uncertainty. This article considers the application of probabilistic electricity price forecasting models to the wholesale market of Great Britain (GB) and compares them to better understand their capabilities and limits. One of the models that this paper considers is a recent novel X-model that predicts the full supply and demand curves from the bid-stack. The advantage of this model is that it better captures price spikes in the data. In this paper, we provide an adjustment to the model to handle data from GB. In addition to this, we then consider and compare two time-series approaches and a simple benchmark. We compare both point forecasts and probabilistic forecasts on real wholesale price data from GB and consider both point and probabilistic measures.

Keywords: price forecasting; day-ahead forecasting; probabilistic price forecasting; electricity prices; supply and demand curves; price spikes; wholesale market (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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