@article{2ddfbd4902a4403393166a0eda386d56,
title = "A very short-term probabilistic prediction interval forecaster for reducing load uncertainty level in smart grids",
abstract = "Very short-term load demand forecasters are essential for power systems{\textquoteright} decision makers in real-time dispatching. These tools allow traditional network operators to maintain power sys-tems{\textquoteright} safety and stability and provide customers energy with high reliability. Although research has traditionally focused on developing point forecasters, these tools do not provide complete information because they do not estimate the deviation between actual and predicted values. There-fore, the aim of this paper is to develop a very short-term probabilistic prediction interval forecaster to reduce decision makers{\textquoteright} uncertainty by computing the predicted value{\textquoteright}s upper and lower bounds. The proposed forecaster combines an artificial intelligence-based point forecaster with a probabilistic prediction interval algorithm. First, the point forecaster predicts energy demand in the next 15 min and then the prediction interval algorithm calculates the upper and lower bounds with the user{\textquoteright}s chosen confidence level. To examine the reliability of proposed forecaster model and re-sulting interval sharpness, different error metrics, such as prediction interval coverage percentage and a skill score, are computed for 95, 90, and 85% confidence intervals. Results show that the prediction interval coverage percentage is higher than the confidence level in each analysis, which means that the proposed model is valid for practical applications.",
keywords = "Energy demand, Prediction interval, Probabilistic, Smart grid, Very short-term forecaster",
author = "Ferm{\'i}n Rodr{\'i}guez and Najmeh Bazmohammadi and Guerrero, {Josep M.} and Ainhoa Galarza",
note = "Funding Information: Funding: This research was funded by the Basque Government{\textquoteright}s Department of Education for financial support through the Researcher Formation Program; grant number PRE_2019_2_0035. This research was funded by Fundaci{\'o}n Caja Navarra, Obra Social La Caixa and University of Navarra for financial support through the Mobility Research Formation Program; grant number MOVIL-2019-25. This research was funded by the European Union{\textquoteright}s Horizon 2020 research and innovation program under grant agreement No 847054. The authors are grateful for the support and contributions from other members of the AmBIENCe project consortium, from VITO (Belgium), ENEA (Italy), TEKNIKER (Spain), INESC TEC (Portugal), ENERGINVEST (Belgium), EDP CNET (Portugal), and BPIE (Belgium). Further information can be found on the project website (http://ambience-project.eu/, accessed on 27 May 2020). This research was funded by the ELKARTEK program (CODIS-AVA KK-2020/00044) of the Basque Government. J. M. Guerrero was supported by VILLUM FONDEN under the VILLUM Investigator Grant (no. 25920): Center for Research on Microgrids (CROM); www.crom.et.aau.dk. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = mar,
day = "2",
doi = "10.3390/app11062538",
language = "English",
volume = "11",
journal = "Applied Sciences (Switzerland)",
issn = "2076-3417",
publisher = "Balkan Society of Geometers",
number = "6",
}