Special Issue "Ensemble Forecasting Applied to Power Systems"

A special issue of Forecasting (ISSN 2571-9394).

Deadline for manuscript submissions: 28 February 2019

Special Issue Editors

Guest Editor
Prof. Dr. Antonio Bracale

Department of Engineering, University of Napoli Parthenope, Naples, Italy
Website | E-Mail
Interests: load forecasting; energy forecasting; power quality, optimal planning and management; smart grids; power system analysis; signal processing
Guest Editor
Dr. Pasquale De Falco

Department of Engineering, University of Naples Parthenope, Naples, Italy
Website | E-Mail
Interests: load and renewable generation forecasting; distributed energy storage devices; smart grids

Special Issue Information

Dear Colleagues,

Forecasting is a crucial task in planning and managing modern power systems at various levels, such as transmission networks, distribution systems, and smart grids. Many important operations nowadays are scheduled and performed on the basis of predictions of several variables, such as non-controllable generation, loads, energy prices, and power quality indicators. Forecasts at different lead times, ranging from several minutes to several days, are needed in order to suit different applications and scenarios.

The application of forecasting techniques to power systems, in both deterministic and probabilistic frameworks, is yet to be fully explored.

Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques were successfully applied in several frameworks, and have recently started to attract the interest of power system practitioners. The subject is therefore worthy of further investigation.

This Special Issue addresses the development of new, advanced, ensemble forecasting methods applied to power systems. The Special Issue is opened to contributions, developed in both deterministic and probabilistic frameworks, which provide accurate forecasts in terms of spot values, prediction intervals, predictive distributions, predictive quantiles.

We are particularly interested in contributions dealing with forecasting power generated from renewable non-controllable sources (such as solar, wind, and tidal), loads (such as aggregated, individual, domestic, industrial, electrical vehicles), energy prices, and power quality indicators (such as voltage sag and harmonics). Further contributions with adequate level of innovation are encouraged as well.

All of the submitted contributions must demonstrate a theoretical sound framework, presenting also practical applications to actual scenarios.

Prof. Dr. Antonio Bracale
Dr. Pasquale De Falco
Guest Editors

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) is waived for well-prepared manuscripts submitted to this issue. 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.

Keywords

  • Ensemble forecasting
  • Renewable generation forecasting
  • Industrial load forecasting
  • Price forecasting
  • Power quality indices forecasting
  • Smart grids

Published Papers (1 paper)

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Research

Open AccessArticle A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets
Forecasting 2018, 1(1), 26-46; https://doi.org/10.3390/forecast1010003
Received: 18 June 2018 / Revised: 3 July 2018 / Accepted: 9 July 2018 / Published: 12 July 2018
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Abstract
Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors
[...] Read more.
Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods. Full article
(This article belongs to the Special Issue Ensemble Forecasting Applied to Power Systems)
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