Prediction of extreme price occurrences in the German day-ahead electricity market

Hagfors, Lars Ivar; Kamperud, Hilde Hørthe; Paraschiv, Florentina; Prokopczuk, Marcel; Sator, Alma; Westgaard, Sjur

doi:10.1080/14697688.2016.1211794

Cited by 43 publications

(23 citation statements)

References 33 publications

(55 reference statements)

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“…The behavior of fundamentals affects electricity prices, which also follow a yearly pattern. Moreover prices are exposed to extreme fluctuations with both: positive and negative spikes, see Hagfors et al (2016b) for more discussion.…”

Section: Datamentioning

confidence: 99%

“…Although both types of RES have qualitatively similar effect on the supply curve, it is not clear, whether their influence on the price distribution is exactly the same. There are only a few articles, which include both energy sources, see Cludius et al (2014), Paraschiv et al (2014) and Hagfors et al (2016b). They show a price dampening effect of both wind and solar but do not directly compare them.…”

Section: Merit-order Effectmentioning

confidence: 99%

“…The quantile regression (QR) is a well establish econometric approach, which has been successfully used in macro and micro economics (see Koenker and Hallock, 2001). There are a few papers which apply QR for modeling electricity markets in areas such as electricity load (Li et al, 2017), CO 2 emission allowance prices (Hammoudeh et al, 2014) and electricity prices (Hagfors et al, 2016b;Bunn et al, 2016;Nowotarski and Weron, 2015;Maciejowska et al, 2016). In the paper of Hagfors et al (2016b), the UK electricity market is modeled and the dependence of electricity prices on fuel prices and reserve margin is examined.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach

Maciejowska

2020

Energy Economics

108

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The literature on renewable energy sources indicates that an increase of the intermittent wind and solar generation affects significantly the distribution of electricity prices. In this article, the influence of two types of renewable energy sources (wind and solar photo voltaic) on the level and variability of German electricity spot prices is analyzed. The quantile regression models are built to estimate the merit order effect for different quantiles of electricity prices. The results indicate that both types of renewable generations have a similar, negative impact on the price level, approximated by the price median. When the price volatility, measured by the inter-quantile range (IQR), is considered, the outcomes show that wind and solar influence prices differently. Conditional on the level of the total demand, the wind generation would either increase (when the demand is low) or decrease (when the demand is high) the IQR. Meanwhile, the increase of solar power stabilizes the price variance for moderate demand level. Thus, policy supporting the development and integration of RES should search for a balance between the wind and solar power.

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Section: Datamentioning

confidence: 99%

Section: Merit-order Effectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach

Maciejowska

2020

Energy Economics

108

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“…Both approaches have their proponents. For instance, Cuaresma et al (2004), Misiorek et al (2006), Zhou et al (2006), Garcia-Martos et al (2007), Karakatsani and Bunn (2008), Lisi and Nan (2014), Alonso et al (2016), Gaillard et al (2016), Hagfors et al (2016), , Nowotarski and Weron (2016), Uniejewski et al (2016), and Ziel (2016a), among others, advocate the use of sets of 24 (48 or more) models estimated independently for each load period, typically using Ordinary Least Squares (OLS). In the neural network literature, Amjady and Keynia (2009a), Marcjasz et al (2018) and Panapakidis and Dagoumas (2016), among others, use a separate network (i.e., a different parameter set) for each hour of the next day.…”

Section: Introductionmentioning

confidence: 99%

Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks

2018

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We conduct an extensive empirical study on short-term electricity price forecasting (EPF) to address the long-standing question if the optimal model structure for EPF is univariate or multivariate. We provide evidence that despite a minor edge in predictive performance overall, the multivariate modeling framework does not uniformly outperform the univariate one across all 12 considered datasets, seasons of the year or hours of the day, and at times is outperformed by the latter. This is an indication that combining advanced structures or the corresponding forecasts from both modeling approaches can bring a further improvement in forecasting accuracy. We show that this indeed can be the case, even for a simple averaging scheme involving only two models. Finally, we also analyze variable selection for the best performing high-dimensional lasso-type models, thus provide guidelines to structuring better performing forecasting model designs.or a univariate framework, the latter generally perform better for the first half of the day, whereas the former are better in the second half of the day. However, there has been no through, empirical study to date, involving many fine-tuned specifications from both groups. With this paper we want to fill the gap and provide much needed evidence. In particular we want to address three pertinent questions:1. Which modeling frameworkmultivariate or univariate -is better for EPF? 2. If one of them is better, is it better across all hours, seasons of the year and markets? 3. How many and which past values of the spot price process should be used in EPF models?The remainder of the paper is structured as follows. In Section 2 we thoroughly discuss the univariate and multivariate modeling frameworks, which are driven by different data-format perspectives. This is a crucial, conceptual part of the paper, which sets ground for the empirical analysis in the following Sections. In Section 3 we briefly describe the 12 price series used and present the area hyperbolic sine transform for stabilizing the variance of spot price data. In Section 4 we define 10 forecasting models representing eight model classes: (C1) the mean values of the past prices, (C2) similar-day techniques, (C3) sets of 24 parsimonious, interrelated autoregressive (AR) structures (so-called expert models), (C4) sets of 24 univariate AR models, (C5) vector autoregressive (VAR) models, (C6) sets of 24 parameter-rich, interrelated AR models estimated using the least absolute shrinkage and selection operator (i.e., lasso or LASSO; which shrinks to zero the coefficients of redundant explanatory variables), (C7) univariate AR models and (C8) univariate, parameter-rich AR models estimated using the lasso. In Section 5 we evaluate their performance on the basis of the Mean Absolute Error (MAE), the mean percentage deviation from the best (m.p.d.f.b.) model and using two variants of the Diebold and Mariano (1995) test for significant differences in the forecasting performance. We also discuss variable selection for the best performin...

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“…Firstly, instead of the logarithm we used one of four VSTs defined in Section 2.3, which can also be applied to price series with negative values, like those from the German European Energy Exchange (EEX) [25]. Secondly, the order of data transformation and decomposition was swapped.…”

Section: The Forecasting Frameworkmentioning

confidence: 99%

Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models

Uniejewski

Weron

2018

Energies

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Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models that are generally better than those obtained from other variable selection schemes. By conducting an empirical study involving datasets from two major power markets (Nord Pool and PJM Interconnection), three expert models, two multi-parameter regression (called baseline) models and four variance stabilizing transformations combined with the seasonal component approach, we discuss the optimal way of implementing the LASSO. We show that using a complex baseline model with nearly 400 explanatory variables, a well chosen variance stabilizing transformation (asinh or N-PIT), and a procedure that recalibrates the LASSO regularization parameter once or twice a day indeed leads to significant accuracy gains compared to the typically considered EPF models. Moreover, by analyzing the structures of the best LASSO-estimated models, we identify the most important explanatory variables and thus provide guidelines to structuring better performing models.

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Prediction of extreme price occurrences in the German day-ahead electricity market

Cited by 43 publications

References 33 publications

Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach

Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach

Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks

Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models

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