Special Issue "Forecasting Prices in Power Markets"
Deadline for manuscript submissions: 30 June 2022.
Interests: energy analytics; electricity price and load forecasting; energy markets; renewable energy; forecasting in water and environmental sciences; high-dimensional time series; seasonal time series; probabilistic forecasting; evaluation; risk and portfolio management
Special Issues and Collections in MDPI journals
Special Issue in Energies: Uncertainties and Risk Management in Competitive Energy Markets
Interests: electricity markets’ data analysis; electricity price forecasting; energy markets; renewable energy; high-dimensional time series; probabilistic forecasting; evaluation
The increasing liberalization and interconnection of power markets, and the growing importance of renewable energies, storage and demand response lead to complex electricity price formation on power markets. To reduce carbon emissions and the impact of climate change, accurate forecasts are required for efﬁcient decision making. This concerns all forecasting horizons, ranging from short-term horizons with substantial impact from meteorological conditions, whereas long-term forecasts are mainly inﬂuenced by economical, regulatory and technological risk. Power has unique characteristics (especially concerning required system balance and storage opportunities) not found in other commodities. Usually, electricity price data may exhibit speciﬁc characteristics, like
(i) (time-varying) autoregressive effects and (in)stationarity
(ii) calendar effects (daily, weekly and annual seasonality, holiday effects, clockchange)
(iii) (time-varying) volatility and higher moment effects
(iv) (positive and negative) price spikes
(v) price clustering.
Some of these effects may be explained by external input data available at prediction time. However, electricity price forecasting remains a challenging task. We welcome scientiﬁc contributions on forecasting power/electricity prices that give insights into a better understanding of power price dynamics for all kinds of forecasting horizons and power markets (e.g., derivative markets, spot markets, day-ahead markets, intraday and balancing markets). We particularly welcome contributions in the area of probabilistic forecasting using all kinds of methods. This may be suitable for statistical time series prediction methods or machine learning methods, like gradient boosting machines (GBM) or artiﬁcial neural networks (ANN). In this Special Issue, we invite submissions exploring cutting-edge research and recent advances that address the above and related challenges.
Prof. Dr. Florian Ziel
Prof. Dr. Peru Muniain
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- power price formation
- electricity price forecasting
- predictive analytics
- probabilistic forecasting
- machine learning
- neural networks
- statistical models