Ensemble forecasting for intraday electricity prices: Simulating trajectories

Narajewski, Michał; Ziel, Florian

doi:10.1016/j.apenergy.2020.115801

Cited by 54 publications

(75 citation statements)

References 57 publications

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“…Thus, changes in market fundamentals are also associated with greater price fluctuations. The influence of the variable Foreign is consistent with the thoughts of [23,25]. So far, the influence of this variable is relatively small, but considering the constantly changing market and projects to create a Europe-wide intraday market, its relevance could increase.…”

supporting

confidence: 76%

“…The transnational trading of the continuous intraday market was addressed by [23], who defined the dispersion as the volume-weighted standard deviation of individual transactions. An analysis of the ID 3 prices of the individual contracts, considering the individual trades, was carried out by [24], and [25] were engaged in price forecasting of hourly contracts. A mathematical model for intraday power trading that involves both renewable and conventional generation was presented by [26].…”

Section: Literature Reviewmentioning

confidence: 99%

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Volatility and Dispersion of Hourly Electricity Contracts on the German Continuous Intraday Market

Baule

Naumann

2021

Energies

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Intraday electricity trading on the continuous intraday market of EPEX SPOT is particularly well suited for the rebalancing of energy production. We analyzed the volatility and dispersion of individual hourly contracts, taking into account the particularities of the market, due to which the standard volatility measure from financial time series cannot be applied. We used and analyzed five measures for price fluctuations, which turned out to be similarly well suited for electricity contracts, with small differences. We then identified fundamental drivers of price fluctuations: the relative share of wind in the overall mix increased dispersion. In addition, price dispersion was positively correlated with the traded volume as well as the absolute difference between the day-ahead auction price and the volume-weighted intraday price. We furthermore analyzed the timely structure of price fluctuations to identify forecast indicators for a contract’s peak trading hour before maturity, finding that trading-related variables are more important to forecast price fluctuations than fundamental factors. With lagged realizations and additional external drivers, forecast regressions reached an adjusted R2 of 0.479 for volatility and around 0.3 for the dispersion measures.

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supporting

confidence: 76%

Section: Literature Reviewmentioning

confidence: 99%

Volatility and Dispersion of Hourly Electricity Contracts on the German Continuous Intraday Market

Baule

Naumann

2021

Energies

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“…The authors of [9] forecasted quantiles of the German ID market price distribution from three hours up to 30 minutes before delivery, using linear regression and neural networks. A model that uses a combination of GAMLSS (Generalised Additive Models for Location Scale and Shape) models and the logit-lasso (least absolute shrinkage and selection operator) estimation techniques, is applied in [10], for forecasting German CID price distribution in the last 3 hours of trading.…”

Section: A Literature Reviewmentioning

confidence: 99%

Analysing Trading Trends in Continuous Intraday Electricity Markets

Shinde

Kouveliotis-Lysikatos

Amelin

2021

2021 56th International Universities Power Engineering Conference (UPEC)

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The intraday electricity market provides the possibility to trade closer to real-time, aiming at assisting the market participants in improving their market position, based on their updated forecasts. The growing popularity of the intraday markets coupled with the single intraday coupling (SIDC) project in Europe drives certain trends and trading behaviours, that are analysed in this paper. Further, we present different approaches to transform the price data of the historical trades into price time series. The price time series obtained is then leveraged to derive insights on the correlation of the prices in the day-ahead and intraday market; wind power and hydropower generation, and demand time series.Index Terms-Intraday prices, Continuous intraday electricity market, intraday price analysis, time series analysis.

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“…Related works differ with respect to which prices are subject to forecasting. Forecasts often address spot prices where the hourly price of the intraday (Narajewski and Ziel, 2019;Uniejewski et al, 2019;Janke and Steinke, 2019;Narajewski and Ziel, 2020) or day-ahead market (Maciejowska et al, 2019;Marcjasz et al, 2019) needs to be predicted. These predictions are especially applicable to the day-to-day operations of market participants who are concerned with managing power generation and hedging risks (Weron, 2014).…”

Section: Predicted Price Variablesmentioning

confidence: 99%

“…Example use cases include sales forecasts in inventory management (Kraus et al, 2020) and forecasts of stock prices (Oztekin et al, 2016) or macroeconomic indicators (Feuerriegel and Gordon, 2019). In the context of electricity price forecasting, different predictive models have been used, for instance, neural networks (Cruz et al, 2011;Panapakidis and Dagoumas, 2016;Szkuta et al, 1999;Ugurlu et al, 2018), regularization (Ludwig et al, 2015;Uniejewski et al, 2016Uniejewski et al, , 2019Marcjasz et al, 2020), tree-based approaches (Feuerriegel et al, 2014;Ludwig et al, 2015) or ensembles (Narajewski and Ziel, 2020;Agrawal et al, 2019). These largely achieve a lower forecast error.…”

Section: Predictive Modelsmentioning

confidence: 99%

Forecasting electricity prices with machine learning: predictor sensitivity

Naumzik

Feuerriegel

2020

IJESM

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Purpose Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts.. Design/methodology/approach This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis. Findings This study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior. Research limitations/implications The performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting. Practical implications When developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors. Originality/value The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.

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Ensemble forecasting for intraday electricity prices: Simulating trajectories

Cited by 54 publications

References 57 publications

Volatility and Dispersion of Hourly Electricity Contracts on the German Continuous Intraday Market

Volatility and Dispersion of Hourly Electricity Contracts on the German Continuous Intraday Market

Analysing Trading Trends in Continuous Intraday Electricity Markets

Forecasting electricity prices with machine learning: predictor sensitivity

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