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Reliable Solar Irradiance Forecasting Approach Based on Choquet Integral and Deep LSTMs

  • Dades identificatives

    Identificador: imarina:9138985
    Autors:
    Abdel-Nasser MMahmoud KLehtonen M
    Resum:
    © 2005-2012 IEEE. The intermittent nature associated with photovoltaic (PV) generation is a challenging problem for the optimal planning and efficient management in smart grids. A reliable forecasting model of solar irradiance can play an essential role in allowing high PV penetrations without degrading the grid performance. For this purpose, most related works either use individual forecasting models or ensemble approaches (e.g., weighted average), ignoring the interaction between the values to be aggregated and thus may worsen the forecasting reliability. Differently, in this article, we propose a reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral. This novel combination has the following features: 1) LSTM models can achieve accurate predictions because they model the temporal changes in solar irradiance, thanks to their recurrent architecture and memory units, and 2) the Choquet integral can model the interaction between the inputs to be aggregated through a fuzzy measure. This aggregation technique can determine the largest consistency among the conflicting forecasting results, taking advantage of each individual model. To demonstrate the effectiveness of the proposed approach, we compare it with several forecasting methods using six realistic datasets collected from different sites in Finland in which solar irradiance is intermittent. The comparison reveals the high reliability of the proposed forecasting model with different sites and solar profiles.
  • Altres:

    Autor segons l'article: Abdel-Nasser M; Mahmoud K; Lehtonen M
    Departament: Enginyeria Informàtica i Matemàtiques
    e-ISSN: 1941-0050
    Autor/s de la URV: Abdelnasser Mohamed Mahmoud, Mohamed
    Paraules clau: Photovoltaic (pv) Irradiance forecasting Deep long short-term memory (lstm) Choquet integral
    Resum: © 2005-2012 IEEE. The intermittent nature associated with photovoltaic (PV) generation is a challenging problem for the optimal planning and efficient management in smart grids. A reliable forecasting model of solar irradiance can play an essential role in allowing high PV penetrations without degrading the grid performance. For this purpose, most related works either use individual forecasting models or ensemble approaches (e.g., weighted average), ignoring the interaction between the values to be aggregated and thus may worsen the forecasting reliability. Differently, in this article, we propose a reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral. This novel combination has the following features: 1) LSTM models can achieve accurate predictions because they model the temporal changes in solar irradiance, thanks to their recurrent architecture and memory units, and 2) the Choquet integral can model the interaction between the inputs to be aggregated through a fuzzy measure. This aggregation technique can determine the largest consistency among the conflicting forecasting results, taking advantage of each individual model. To demonstrate the effectiveness of the proposed approach, we compare it with several forecasting methods using six realistic datasets collected from different sites in Finland in which solar irradiance is intermittent. The comparison reveals the high reliability of the proposed forecasting model with different sites and solar profiles.
    Àrees temàtiques: Interdisciplinar Information systems Engineering, industrial Engenharias iv Engenharias iii Electrical and electronic engineering Control and systems engineering Computer science, interdisciplinary applications Computer science applications Ciência da computação Automation & control systems
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 1551-3203
    Adreça de correu electrònic de l'autor: mohamed.abdelnasser@urv.cat
    Identificador de l'autor: 0000-0002-1074-2441
    Data d'alta del registre: 2024-07-27
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://ieeexplore.ieee.org/document/9097938
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Ieee Transactions On Industrial Informatics. 17 (3): 1873-1881
    Referència de l'ítem segons les normes APA: Abdel-Nasser M; Mahmoud K; Lehtonen M (2021). Reliable Solar Irradiance Forecasting Approach Based on Choquet Integral and Deep LSTMs. Ieee Transactions On Industrial Informatics, 17(3), 1873-1881. DOI: 10.1109/TII.2020.2996235
    DOI de l'article: 10.1109/TII.2020.2996235
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2021
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Automation & Control Systems,Computer Science Applications,Computer Science, Interdisciplinary Applications,Control and Systems Engineering,Electrical and Electronic Engineering,Engineering, Industrial,Information Systems
    Photovoltaic (pv)
    Irradiance forecasting
    Deep long short-term memory (lstm)
    Choquet integral
    Interdisciplinar
    Information systems
    Engineering, industrial
    Engenharias iv
    Engenharias iii
    Electrical and electronic engineering
    Control and systems engineering
    Computer science, interdisciplinary applications
    Computer science applications
    Ciência da computação
    Automation & control systems
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