Electricity price forecasting using sale and purchase curves: The X-Model

Florian Ziel and Rick Steinert

Energy Economics, 2016, vol. 59, issue C, 435-454

Abstract: Our paper aims to model and forecast the electricity price by taking a completely new perspective on the data. It will be the first approach which is able to combine the insights of market structure models with extensive and modern econometric analysis. Instead of directly modeling the electricity price as it is usually done in time series or data mining approaches, we model and utilize its true source: the sale and purchase curves of the electricity exchange. We will refer to this new model as X-Model, as almost every deregulated electricity price is simply the result of the intersection of the electricity supply and demand curve at a certain auction. Therefore we show an approach to deal with a tremendous amount of auction data, using a subtle data processing technique as well as dimension reduction and lasso based estimation methods. We incorporate not only several known features, such as seasonal behavior or the impact of other processes like renewable energy, but also completely new elaborated stylized facts of the bidding structure. Our model is able to capture the non-linear behavior of the electricity price, which is especially useful for predicting huge price spikes. Using simulation methods we show how to derive prediction intervals for probabilistic forecasting. We describe and show the proposed methods for the day-ahead EPEX spot price of Germany and Austria.

Keywords: Electricity price forecasting; Supply and demand curves; Price spikes; Auction data; Bidding behavior; Probabilistic forecasting (search for similar items in EconPapers)
JEL-codes: C5 C51 C57 C58 Q21 Q4 Q41 Q47 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (53)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:59:y:2016:i:c:p:435-454

DOI: 10.1016/j.eneco.2016.08.008

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