Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets

Ziel, Florian; Steinert, Rick; Husmann, Sven

doi:10.1016/j.eneco.2015.08.005

Cited by 47 publications

(35 citation statements)

References 26 publications

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“…Therefore, none of the considered models performed better than the naive most recent value. This is very similar to the relation between the German-Austrian EXAA and EPEX day-ahead markets that is exhibited by Ziel et al (2015b).…”

Section: Discussionsupporting

confidence: 77%

Econometric modelling and forecasting of intraday electricity prices

Narajewski

Ziel

2020

Journal of Commodity Markets

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In the following paper, we analyse the ID 3 -Price in the German Intraday Continuous electricity market using an econometric time series model. A multivariate approach is conducted for hourly and quarter-hourly products separately. We estimate the model using lasso and elastic net techniques and perform an out-of-sample, very short-term forecasting study. The model's performance is compared with benchmark models and is discussed in detail. Forecasting results provide new insights to the German Intraday Continuous electricity market regarding its efficiency and to the ID 3 -Price behaviour.

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Section: Discussionsupporting

confidence: 77%

Econometric modelling and forecasting of intraday electricity prices

Narajewski

Ziel

2020

Journal of Commodity Markets

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“…It is traded everyday at 10:12 and the prices are known at 10:20 for market participants, which means they are especially known in advance to the EPEX auction at 12:00. Ziel et al (2015b) show that the very simple naive estimator X price d,h = X EXAA,price…”

Section: Resultsmentioning

confidence: 99%

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

Ziel

Steinert

2016

Energy Economics

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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.

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“…Based on the fact that the Energy Exchange Austria (EXAA) discloses day-ahead prices before most of the other European markets, Ref. [20] analyzed the relationship between EXAA day-ahead electricity price and other day-ahead electricity prices in Europe. The authors focus on these special markets that settle their prices after the EXAA, which makes traders able to include the EXAA price into their inference, that is, forecast the price of some other exchanges by incorporating the impact of the EXAA price.…”

Section: Market Integrationmentioning

confidence: 99%

“…It is named as LSTM-DNN in our experiments. • Persistent model, persistent EXAA-based model, and Univariate AR(p) proposed by [20], respectively, abbreviated as naïve, naïve-EXAA, and AR(p) in our experiments. In naïve mode, the electricity price is estimated to be the same as 168 h ago (usually representing one week).…”

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BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market

Chen

Wang

et al. 2019

Energies

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For the benefit from accurate electricity price forecasting, not only can various electricity market stakeholders make proper decisions to gain profit in a competitive environment, but also power system stability can be improved. Nevertheless, because of the high volatility and uncertainty, it is an essential challenge to accurately forecast the electricity price. Considering that recurrent neural networks (RNNs) are suitable for processing time series data, in this paper, we propose a bidirectional long short-term memory (LSTM)-based forecasting model, BRIM, which splits the state neurons of a regular RNN into two parts: the forward states (using the historical electricity price information) are designed for processing the data in positive time direction and backward states (using the future price information available at inter-connected markets) for the data in negative time direction. Moreover, due to the fact that inter-connected power exchange markets show a common trend for other neighboring markets and can provide signaling information for each other, it is sensible to incorporate and exploit the impact of the neighboring markets on forecasting accuracy of electricity price. Specifically, future electricity prices of the interconnected market are utilized both as input features for forward LSTM and backward LSTM. By testing on day-ahead electricity prices in the European Power Exchange (EPEX), the experimental results show the superiority of the proposed method BRIM in enhancing predictive accuracy in comparison with the various benchmarks, and moreover Diebold-Mariano (DM) shows that the forecast accuracy of BRIM is not equal to other forecasting models, and thus indirectly demonstrates that BRIM statistically significantly outperforms other schemes.

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Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets

Cited by 47 publications

References 26 publications

Econometric modelling and forecasting of intraday electricity prices

Econometric modelling and forecasting of intraday electricity prices

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

BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market

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