arXiv 23 May 2022 · Finance — Statistical Finance · publishedEnergies (2022) · 29 citations (OpenAlex)
arXiv:2205.11439 · PDF · DOI · OpenAlex · Extracted main text
The exponential growth of renewable energy capacity has brought much uncertainty to electricity prices and to electricity generation. To address this challenge, the energy exchanges have been developing further trading possibilities, especially the intraday and balancing markets. For an energy trader participating in both markets, the forecasting of imbalance prices is of particular interest. Therefore, in this manuscript we conduct a very short-term probabilistic forecasting of imbalance prices, contributing to the scarce literature in this novel subject. The forecasting is performed 30 minutes before the delivery, so that the trader might still choose the trading place. The distribution of the imbalance prices is modelled and forecasted using methods well-known in the electricity price forecasting literature: lasso with bootstrap, gamlss, and probabilistic neural networks. The methods are compared with a naive benchmark in a meaningful rolling window study. The results provide evidence of the efficiency between the intraday and balancing markets as the sophisticated methods do not substantially overperform the intraday continuous price index. On the other hand, they significantly improve the empirical coverage. The analysis was conducted on the German market, however it could be easily applied to any other market of similar structure.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | M. Narajewski and F. Ziel (2020) Econometric modelling and forecasting of intraday electricity prices | 1.000 | 11 | 5 | 100% |
| 2 | M. Narajewski and F. Ziel (2020) Ensemble forecasting for intraday electricity prices: Simulating trajectories | 1.000 | 9 | 4 | 100% |
| 3 | B. Uniejewski, G. Marcjasz, and R. Weron (2019) Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO | 1.000 | 5 | 4 | 100% |
| 4 | G. Marcjasz, B. Uniejewski, and R. Weron (2020) Beating the nave—Combining LASSO with nave intraday electricity price forecasts | 1.000 | 5 | 3 | 100% |
| 5 | J. Nowotarski and R. Weron (2018) Recent advances in electricity price forecasting: A review of probabilistic forecasting | 0.928 | 4 | 3 | 100% |
| 6 | F. Ziel and R. Weron (2018) Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks | 0.928 | 4 | 3 | 100% |
| 7 | M. Narajewski and F. Ziel (2021) Optimal bidding on hourly and quarter-hourly day-ahead electricity price auctions: trading large volumes of power with market im… | 0.843 | 3 | 3 | 100% |
| 8 | J. Lago, G. Marcjasz, B. De Schutter, and R. Weron (2021) Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark | 0.811 | 4 | 2 | 100% |
| 9 | F. Ziel (2016) Forecasting electricity spot prices using lasso: On capturing the autoregressive intraday structure | 0.737 | 3 | 2 | 100% |
| 10 | J. Viehmann (2017) State of the German Short-Term Power Market | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 57 scored citations.