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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: orcid Bianca Magalhães;
    Bianca Magalhães
    ORCID
    Harvested from ORCID Public Data File

    Bianca Magalhães in OpenAIRE
    orcid Pedro Bento;
    Pedro Bento
    ORCID
    Harvested from ORCID Public Data File

    Pedro Bento in OpenAIRE
    orcid José Pombo;
    José Pombo
    ORCID
    Harvested from ORCID Public Data File

    José Pombo in OpenAIRE
    orcid Maria do Rosário Calado;
    Maria do Rosário Calado
    ORCID
    Harvested from ORCID Public Data File

    Maria do Rosário Calado in OpenAIRE
    +1 Authors

    Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energies
    Article . 2024 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energies
    Article . 2024
    Data sources: DOAJ
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    9
    citations9
    popularityAverage
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Energies
      Article . 2024 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Energies
      Article . 2024
      Data sources: DOAJ
      addClaim
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: orcid Bianca Magalhães;
    Bianca Magalhães
    ORCID
    Harvested from ORCID Public Data File

    Bianca Magalhães in OpenAIRE
    orcid Pedro Bento;
    Pedro Bento
    ORCID
    Harvested from ORCID Public Data File

    Pedro Bento in OpenAIRE
    orcid José Pombo;
    José Pombo
    ORCID
    Harvested from ORCID Public Data File

    José Pombo in OpenAIRE
    orcid Maria do Rosário Calado;
    Maria do Rosário Calado
    ORCID
    Harvested from ORCID Public Data File

    Maria do Rosário Calado in OpenAIRE
    +1 Authors

    Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energies
    Article . 2024 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energies
    Article . 2024
    Data sources: DOAJ
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    citations9
    popularityAverage
    influenceTop 10%
    impulseTop 10%
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Energies
      Article . 2024 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Energies
      Article . 2024
      Data sources: DOAJ
      addClaim
  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: M.R.A. Calado; orcid José Pombo;
    José Pombo
    ORCID
    Harvested from ORCID Public Data File

    José Pombo in OpenAIRE
    R.P.G. Mendes; orcid P.M.R. Bento;
    P.M.R. Bento
    ORCID
    Harvested from ORCID Public Data File

    P.M.R. Bento in OpenAIRE
    +1 Authors

    Abstract Ocean renewable energy is a promising inexhaustible source of renewable energy, with an estimated harnessing potential of approximately 337 GW worldwide, which could re-shape the power generation mix. As with other sources of renewables, however, wave energy has an intermittent and irregular nature, which is a major concern for power system stability. Consequently, in order to integrate wave energy into power grids, it must be forecasted. This paper proposes using optimised deep learning neural networks to forecast the wave energy flux, and other wave parameters. In particular, we use moth-flame optimisation as the central decision-making unit to configure the deep neural network structure and the proper input data selection. Besides, the moth-flame optimisation algorithm was modified to improve its search space mechanisms. The forecasting skills are assessed using 13 datasets from locations across the Pacific and Atlantic coasts, and the Gulf of Mexico. The proposed optimised deep neural network performs well at all the sites, especially over short-term horizons, where it outperforms statistical and physics-based approaches.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Ocean Engineeringarrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Ocean Engineering
    Article . 2021 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
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    80
    citations80
    popularityTop 1%
    influenceTop 10%
    impulseTop 1%
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Ocean Engineeringarrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Ocean Engineering
      Article . 2021 . Peer-reviewed
      License: Elsevier TDM
      Data sources: Crossref
      addClaim
  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: M.R.A. Calado; orcid José Pombo;
    José Pombo
    ORCID
    Harvested from ORCID Public Data File

    José Pombo in OpenAIRE
    R.P.G. Mendes; orcid P.M.R. Bento;
    P.M.R. Bento
    ORCID
    Harvested from ORCID Public Data File

    P.M.R. Bento in OpenAIRE
    +1 Authors

    Abstract Ocean renewable energy is a promising inexhaustible source of renewable energy, with an estimated harnessing potential of approximately 337 GW worldwide, which could re-shape the power generation mix. As with other sources of renewables, however, wave energy has an intermittent and irregular nature, which is a major concern for power system stability. Consequently, in order to integrate wave energy into power grids, it must be forecasted. This paper proposes using optimised deep learning neural networks to forecast the wave energy flux, and other wave parameters. In particular, we use moth-flame optimisation as the central decision-making unit to configure the deep neural network structure and the proper input data selection. Besides, the moth-flame optimisation algorithm was modified to improve its search space mechanisms. The forecasting skills are assessed using 13 datasets from locations across the Pacific and Atlantic coasts, and the Gulf of Mexico. The proposed optimised deep neural network performs well at all the sites, especially over short-term horizons, where it outperforms statistical and physics-based approaches.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Ocean Engineeringarrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Ocean Engineering
    Article . 2021 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
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    80
    citations80
    popularityTop 1%
    influenceTop 10%
    impulseTop 1%
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Ocean Engineeringarrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Ocean Engineering
      Article . 2021 . Peer-reviewed
      License: Elsevier TDM
      Data sources: Crossref
      addClaim
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: orcid Magalhães, Bianca G.;
    Magalhães, Bianca G.
    ORCID
    Harvested from ORCID Public Data File

    Magalhães, Bianca G. in OpenAIRE
    orcid Bento, Pedro M. R.;
    Bento, Pedro M. R.
    ORCID
    Harvested from ORCID Public Data File

    Bento, Pedro M. R. in OpenAIRE
    orcid Pombo, José;
    Pombo, José
    ORCID
    Harvested from ORCID Public Data File

    Pombo, José in OpenAIRE
    orcid Calado, M. do Rosário;
    Calado, M. do Rosário
    ORCID
    Harvested from ORCID Public Data File

    Calado, M. do Rosário in OpenAIRE
    +1 Authors

    The increasing volatility in electricity markets has reinforced the need for better trading strategies by both sellers and buyers to limit the exposure to losses. Accordingly, this paper proposes an electricity trading strategy based on a mid-term forecast of the average spot price and a risk premium analysis based on this forecast. This strategy can help traders (buyers and sellers) decide whether to trade in the futures market (of varying monthly maturity) or to wait and trade in the spot market. The forecast model consists of an Artificial Neural Network trained with the Long Short Term Memory architecture to predict the average monthly spot prices, using only market price-related data as input variables. Statistical analysis verified the correlation and dependency between variables. The forecast model was trained, validated and tested with price data from the Iberian Electricity Market (MIBEL), in particular the Spanish zone, between January 2015 and August 2019. The last year of this period was reserved for testing the performance of the proposed forecast model and trading strategy. For comparison purposes, the results of a forecasting model trained with the Extreme Learning Machine over the same period are also presented. In addition, the forecasted value of the average monthly spot price was used to perform a risk premium analysis. The results were promising, as they indicated benefits for traders adopting the proposed trading strategy, proving the potential of the forecast model and the risk premium analysis based on this forecast.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Universidade da Beir...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Expert Systems with Applications
    Article . 2023 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    addClaim
    Access Routes
    Green
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    12
    citations12
    popularityTop 10%
    influenceAverage
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    BIP!Powered by BIP!
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    visibilityviews94
    downloaddownloads24
    Powered by Usage counts
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Universidade da Beir...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Expert Systems with Applications
      Article . 2023 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      addClaim
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: orcid Magalhães, Bianca G.;
    Magalhães, Bianca G.
    ORCID
    Harvested from ORCID Public Data File

    Magalhães, Bianca G. in OpenAIRE
    orcid Bento, Pedro M. R.;
    Bento, Pedro M. R.
    ORCID
    Harvested from ORCID Public Data File

    Bento, Pedro M. R. in OpenAIRE
    orcid Pombo, José;
    Pombo, José
    ORCID
    Harvested from ORCID Public Data File

    Pombo, José in OpenAIRE
    orcid Calado, M. do Rosário;
    Calado, M. do Rosário
    ORCID
    Harvested from ORCID Public Data File

    Calado, M. do Rosário in OpenAIRE
    +1 Authors

    The increasing volatility in electricity markets has reinforced the need for better trading strategies by both sellers and buyers to limit the exposure to losses. Accordingly, this paper proposes an electricity trading strategy based on a mid-term forecast of the average spot price and a risk premium analysis based on this forecast. This strategy can help traders (buyers and sellers) decide whether to trade in the futures market (of varying monthly maturity) or to wait and trade in the spot market. The forecast model consists of an Artificial Neural Network trained with the Long Short Term Memory architecture to predict the average monthly spot prices, using only market price-related data as input variables. Statistical analysis verified the correlation and dependency between variables. The forecast model was trained, validated and tested with price data from the Iberian Electricity Market (MIBEL), in particular the Spanish zone, between January 2015 and August 2019. The last year of this period was reserved for testing the performance of the proposed forecast model and trading strategy. For comparison purposes, the results of a forecasting model trained with the Extreme Learning Machine over the same period are also presented. In addition, the forecasted value of the average monthly spot price was used to perform a risk premium analysis. The results were promising, as they indicated benefits for traders adopting the proposed trading strategy, proving the potential of the forecast model and the risk premium analysis based on this forecast.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Universidade da Beir...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Expert Systems with Applications
    Article . 2023 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Access Routes
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    12
    citations12
    popularityTop 10%
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    visibilityviews94
    downloaddownloads24
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Universidade da Beir...arrow_drop_down
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      Expert Systems with Applications
      Article . 2023 . Peer-reviewed
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    Authors: orcid Nunes, H.G.G.;
    Nunes, H.G.G.
    ORCID
    Harvested from ORCID Public Data File

    Nunes, H.G.G. in OpenAIRE
    orcid Pombo, José Álvaro Nunes;
    Pombo, José Álvaro Nunes
    ORCID
    Harvested from ORCID Public Data File

    Pombo, José Álvaro Nunes in OpenAIRE
    orcid Bento, P.M.R.;
    Bento, P.M.R.
    ORCID
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    Bento, P.M.R. in OpenAIRE
    orcid bw Mariano, S.;
    Mariano, S.
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Mariano, S. in OpenAIRE
    +1 Authors

    Abstract To properly evaluate, control and optimize photovoltaic (PV) systems, it is crucial to accurately estimate the equivalent electric circuit parameters from the respective mathematical models that characterize the PV cells or modules behavior. This is currently a hot research topic that has attracted the attention of numerous researchers. In this paper, we propose a new hybrid methodology that combines diversification and intensification mechanisms from different metaheuristics (MHs) to estimate PV parameters precisely. The proposed methodology has the capacity to adapt to the specific optimization problem and maintain diversity when building solutions, thus mitigating premature convergence and population stagnation. This methodology can incorporate several MHs (two or more swarms) with different potentialities, enabling a good balance between diversification and intensification mechanisms. Furthermore, it is able to explore a multidimensional search space in different regions simultaneously. To validate its performance, the proposed methodology was compared with other well-established MHs in several benchmark functions, and used to estimate PV parameters in single and double-diode models in two case studies, the first using standard literature data, and the second using measured data from a real application with and without the occurrence of partial shading. The proposed methodology was able to find highly accurate solutions with reduced computational cost and high reliability. Comparisons with the other MHs demonstrate that the proposed methodology presents a very competitive performance when solving the PV parameter estimation problem.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao uBibliorum Repositor...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Energy Conversion and Management
    Article . 2019 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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    citations102
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao uBibliorum Repositor...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      Energy Conversion and Management
      Article . 2019 . Peer-reviewed
      License: Elsevier TDM
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    Authors: orcid Nunes, H.G.G.;
    Nunes, H.G.G.
    ORCID
    Harvested from ORCID Public Data File

    Nunes, H.G.G. in OpenAIRE
    orcid Pombo, José Álvaro Nunes;
    Pombo, José Álvaro Nunes
    ORCID
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    Pombo, José Álvaro Nunes in OpenAIRE
    orcid Bento, P.M.R.;
    Bento, P.M.R.
    ORCID
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    Bento, P.M.R. in OpenAIRE
    orcid bw Mariano, S.;
    Mariano, S.
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Mariano, S. in OpenAIRE
    +1 Authors

    Abstract To properly evaluate, control and optimize photovoltaic (PV) systems, it is crucial to accurately estimate the equivalent electric circuit parameters from the respective mathematical models that characterize the PV cells or modules behavior. This is currently a hot research topic that has attracted the attention of numerous researchers. In this paper, we propose a new hybrid methodology that combines diversification and intensification mechanisms from different metaheuristics (MHs) to estimate PV parameters precisely. The proposed methodology has the capacity to adapt to the specific optimization problem and maintain diversity when building solutions, thus mitigating premature convergence and population stagnation. This methodology can incorporate several MHs (two or more swarms) with different potentialities, enabling a good balance between diversification and intensification mechanisms. Furthermore, it is able to explore a multidimensional search space in different regions simultaneously. To validate its performance, the proposed methodology was compared with other well-established MHs in several benchmark functions, and used to estimate PV parameters in single and double-diode models in two case studies, the first using standard literature data, and the second using measured data from a real application with and without the occurrence of partial shading. The proposed methodology was able to find highly accurate solutions with reduced computational cost and high reliability. Comparisons with the other MHs demonstrate that the proposed methodology presents a very competitive performance when solving the PV parameter estimation problem.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao uBibliorum Repositor...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Energy Conversion and Management
    Article . 2019 . Peer-reviewed
    License: Elsevier TDM
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao uBibliorum Repositor...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Energy Conversion and Management
      Article . 2019 . Peer-reviewed
      License: Elsevier TDM
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    Authors: orcid Pedro Bento;
    Pedro Bento
    ORCID
    Harvested from ORCID Public Data File

    Pedro Bento in OpenAIRE
    orcid José Pombo;
    José Pombo
    ORCID
    Harvested from ORCID Public Data File

    José Pombo in OpenAIRE
    orcid Maria do Rosário Calado;
    Maria do Rosário Calado
    ORCID
    Harvested from ORCID Public Data File

    Maria do Rosário Calado in OpenAIRE
    orcid bw Sílvio Mariano;
    Sílvio Mariano
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Sílvio Mariano in OpenAIRE

    AbstractClimate change “fuelled” by anthropogenic causes has been identified as the greatest threat faced by societies. In this respect, the roadmap to a “greener” generation mix certainly includes a greater heterogeneity in terms of renewable energy sources. In this regard, one of the leading candidates is ocean wave energy. One of the issues with renewables in general is their unpredictably and variability, as it is crucial to address the subject of wave power forecasting, to facilitate a future market integration. Hence, to tackle this prediction problem, a new approach to short‐term wave power forecasting is proposed, based on deep learning capabilities. These highly popular networks were traditionally developed to deal with images (2D data), so the authors discuss all the necessary implementation and design details to employ these networks with 1D input data, to solve a regression‐based problem. These case‐studies include wave data from three different locations. The proposed approach was tested across all seasons of the year, revealing the suitability to extract the relevant input data dependencies from the time‐series. As such, especially for horizons up to 6 h, the proposed approach outperforms other conventional methods.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ IET Renewable Power ...arrow_drop_down
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    IET Renewable Power Generation
    Article . 2021 . Peer-reviewed
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    IET Renewable Power Generation
    Article . 2021
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      IET Renewable Power Generation
      Article . 2021 . Peer-reviewed
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      Article . 2021
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    Authors: orcid Pedro Bento;
    Pedro Bento
    ORCID
    Harvested from ORCID Public Data File

    Pedro Bento in OpenAIRE
    orcid José Pombo;
    José Pombo
    ORCID
    Harvested from ORCID Public Data File

    José Pombo in OpenAIRE
    orcid Maria do Rosário Calado;
    Maria do Rosário Calado
    ORCID
    Harvested from ORCID Public Data File

    Maria do Rosário Calado in OpenAIRE
    orcid bw Sílvio Mariano;
    Sílvio Mariano
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Sílvio Mariano in OpenAIRE

    AbstractClimate change “fuelled” by anthropogenic causes has been identified as the greatest threat faced by societies. In this respect, the roadmap to a “greener” generation mix certainly includes a greater heterogeneity in terms of renewable energy sources. In this regard, one of the leading candidates is ocean wave energy. One of the issues with renewables in general is their unpredictably and variability, as it is crucial to address the subject of wave power forecasting, to facilitate a future market integration. Hence, to tackle this prediction problem, a new approach to short‐term wave power forecasting is proposed, based on deep learning capabilities. These highly popular networks were traditionally developed to deal with images (2D data), so the authors discuss all the necessary implementation and design details to employ these networks with 1D input data, to solve a regression‐based problem. These case‐studies include wave data from three different locations. The proposed approach was tested across all seasons of the year, revealing the suitability to extract the relevant input data dependencies from the time‐series. As such, especially for horizons up to 6 h, the proposed approach outperforms other conventional methods.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ IET Renewable Power ...arrow_drop_down
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    IET Renewable Power Generation
    Article . 2021 . Peer-reviewed
    License: CC BY
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    IET Renewable Power Generation
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    IET Renewable Power Generation
    Article . 2021
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    Authors: orcid Pedro M. R. Bento;
    Pedro M. R. Bento
    ORCID
    Harvested from ORCID Public Data File

    Pedro M. R. Bento in OpenAIRE
    orcid Jose A. N. Pombo;
    Jose A. N. Pombo
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    Harvested from ORCID Public Data File

    Jose A. N. Pombo in OpenAIRE
    orcid bw Maria R. A. Calado;
    Maria R. A. Calado
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    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Maria R. A. Calado in OpenAIRE
    orcid Silvio J. P. S. Mariano;
    Silvio J. P. S. Mariano
    ORCID
    Harvested from ORCID Public Data File

    Silvio J. P. S. Mariano in OpenAIRE

    Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and its participants need to better inform their decisions. Hence, it is important to continue advancing in terms of forecasting accuracy and consistency. This paper presents a new deep learning-based ensemble methodology for 24 h ahead load forecasting, where an automatic framework is proposed to select the best Box-Jenkins models (ARIMA Forecasters), from a wide-range of combinations. The method is distinct in its parameters but more importantly in considering different batches of historical (training) data, thus benefiting from prediction models focused on recent and longer load trends. Afterwards, these accurate predictions, mainly the linear components of the load time-series, are fed to the ensemble Deep Forward Neural Network. This flexible type of network architecture not only functions as a combiner but also receives additional historical and auxiliary data to further its generalization capabilities. Numerical testing using New England market data validated the proposed ensemble approach with diverse base forecasters, achieving promising results in comparison with other state-of-the-art methods.

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    Energies
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    Authors: orcid Pedro M. R. Bento;
    Pedro M. R. Bento
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    Pedro M. R. Bento in OpenAIRE
    orcid Jose A. N. Pombo;
    Jose A. N. Pombo
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    Jose A. N. Pombo in OpenAIRE
    orcid bw Maria R. A. Calado;
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    Maria R. A. Calado in OpenAIRE
    orcid Silvio J. P. S. Mariano;
    Silvio J. P. S. Mariano
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    Silvio J. P. S. Mariano in OpenAIRE

    Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and its participants need to better inform their decisions. Hence, it is important to continue advancing in terms of forecasting accuracy and consistency. This paper presents a new deep learning-based ensemble methodology for 24 h ahead load forecasting, where an automatic framework is proposed to select the best Box-Jenkins models (ARIMA Forecasters), from a wide-range of combinations. The method is distinct in its parameters but more importantly in considering different batches of historical (training) data, thus benefiting from prediction models focused on recent and longer load trends. Afterwards, these accurate predictions, mainly the linear components of the load time-series, are fed to the ensemble Deep Forward Neural Network. This flexible type of network architecture not only functions as a combiner but also receives additional historical and auxiliary data to further its generalization capabilities. Numerical testing using New England market data validated the proposed ensemble approach with diverse base forecasters, achieving promising results in comparison with other state-of-the-art methods.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
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    Energies
    Article . 2021 . Peer-reviewed
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    Energies
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    Energies
    Article . 2021
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    Authors: orcid Bento, P.M.R.;
    Bento, P.M.R.
    ORCID
    Harvested from ORCID Public Data File

    Bento, P.M.R. in OpenAIRE
    orcid Pombo, José Álvaro Nunes;
    Pombo, José Álvaro Nunes
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    Pombo, José Álvaro Nunes in OpenAIRE
    orcid Calado, M. Do Rosário;
    Calado, M. Do Rosário
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    Calado, M. Do Rosário in OpenAIRE
    orcid bw Mariano, S.;
    Mariano, S.
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Mariano, S. in OpenAIRE

    Abstract Short-term load forecasting is very important for reliable power system operation, even more so under electricity market deregulation and integration of renewable resources framework. This paper presents a new enhanced method for one day ahead load forecast, combing improved data selection and features extraction techniques (similar/recent day-based selection, correlation and wavelet analysis), which brings more “regularity” to the load time-series, an important precondition for the successful application of neural networks. A combination of Bat and Scaled Conjugate Gradient Algorithms is proposed to improve neural network learning capability. Another feature is the method's capacity to fine-tune neural network architecture and wavelet decomposition, for which there is no optimal paradigm. Numerical testing using the Portuguese national system load, and the regional (state) loads of New England and New York, revealed promising forecasting results in comparison with other state-of-the-art methods, therefore proving the effectiveness of the assembled methodology.

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    Neurocomputing
    Article . 2019 . Peer-reviewed
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      Neurocomputing
      Article . 2019 . Peer-reviewed
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    Authors: orcid Bento, P.M.R.;
    Bento, P.M.R.
    ORCID
    Harvested from ORCID Public Data File

    Bento, P.M.R. in OpenAIRE
    orcid Pombo, José Álvaro Nunes;
    Pombo, José Álvaro Nunes
    ORCID
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    Pombo, José Álvaro Nunes in OpenAIRE
    orcid Calado, M. Do Rosário;
    Calado, M. Do Rosário
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    Calado, M. Do Rosário in OpenAIRE
    orcid bw Mariano, S.;
    Mariano, S.
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    Mariano, S. in OpenAIRE

    Abstract Short-term load forecasting is very important for reliable power system operation, even more so under electricity market deregulation and integration of renewable resources framework. This paper presents a new enhanced method for one day ahead load forecast, combing improved data selection and features extraction techniques (similar/recent day-based selection, correlation and wavelet analysis), which brings more “regularity” to the load time-series, an important precondition for the successful application of neural networks. A combination of Bat and Scaled Conjugate Gradient Algorithms is proposed to improve neural network learning capability. Another feature is the method's capacity to fine-tune neural network architecture and wavelet decomposition, for which there is no optimal paradigm. Numerical testing using the Portuguese national system load, and the regional (state) loads of New England and New York, revealed promising forecasting results in comparison with other state-of-the-art methods, therefore proving the effectiveness of the assembled methodology.

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    Neurocomputing
    Article . 2019 . Peer-reviewed
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      Neurocomputing
      Article . 2019 . Peer-reviewed
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    Authors: orcid Bento, P.M.R.;
    Bento, P.M.R.
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    Bento, P.M.R. in OpenAIRE
    orcid Nunes, H.G.G.;
    Nunes, H.G.G.
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    Nunes, H.G.G. in OpenAIRE
    orcid Álvaro Nunes Pombo, José;
    Álvaro Nunes Pombo, José
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    Álvaro Nunes Pombo, José in OpenAIRE
    orcid bw Mariano, S.;
    Mariano, S.
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    Mariano, S. in OpenAIRE
    +1 Authors

    With an increasing public and governmental awareness regarding environment protection and sustainable resources, hand-in-hand with an escalating electricity demand, the exponential growth of renewable energy generation capacity has been the “answer”. Mitigating the environmental harms associated with the more conventional energy sources such as: coal, oil, gas and nuclear. Nonetheless, some challenges remain, particularly concerning the integration of these technologies into the conventional generation mix. Hybrid energy systems allow a paradigm shift from a concentrated conventional generation to a more distributed one. This paper discusses the optimized PV-wind with hydro and battery storage capabilities for a grid-connected application considering the Short-Term Price Forecast information. The proposed technique has been tested in different scenarios, and results demonstrate the effectiveness of the proposed approach.

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    https://doi.org/10.1109/eeeic....
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      https://doi.org/10.1109/eeeic....
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    Authors: orcid Bento, P.M.R.;
    Bento, P.M.R.
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    Bento, P.M.R. in OpenAIRE
    orcid Nunes, H.G.G.;
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    orcid Álvaro Nunes Pombo, José;
    Álvaro Nunes Pombo, José
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    orcid bw Mariano, S.;
    Mariano, S.
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    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Mariano, S. in OpenAIRE
    +1 Authors

    With an increasing public and governmental awareness regarding environment protection and sustainable resources, hand-in-hand with an escalating electricity demand, the exponential growth of renewable energy generation capacity has been the “answer”. Mitigating the environmental harms associated with the more conventional energy sources such as: coal, oil, gas and nuclear. Nonetheless, some challenges remain, particularly concerning the integration of these technologies into the conventional generation mix. Hybrid energy systems allow a paradigm shift from a concentrated conventional generation to a more distributed one. This paper discusses the optimized PV-wind with hydro and battery storage capabilities for a grid-connected application considering the Short-Term Price Forecast information. The proposed technique has been tested in different scenarios, and results demonstrate the effectiveness of the proposed approach.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao uBibliorum Repositor...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    https://doi.org/10.1109/eeeic....
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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    Authors: orcid Pedro M.R. Bento;
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    Pedro M.R. Bento in OpenAIRE
    orcid Jose A.N. Pombo;
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    orcid Silvio J.P.S. Mariano;
    Silvio J.P.S. Mariano
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    Silvio J.P.S. Mariano in OpenAIRE
    Maria R.A. Calado;
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
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    https://doi.org/10.1109/eeeic/...
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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    orcid J.A.N. Pombo;
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    The ongoing COVID-19 pandemic has established itself has one of the biggest health crises facing humanity. Countries all around the world were forced to adopt unprecedented restrictive measures in order to halt the spread of the virus and safeguard public health. These measures have profoundly changed the way of life and severely affected practically all sectors of activity, with major demand and supply shocks, leading to one of the largest recessions in world history. An essential pillar to the proper functioning of modern societies is energy security, particularly electricity security, which guarantees a reliable and efficient supply of electricity. Energy distributors and utility companies remained operational during mandatory stay-at-home orders, to ensure an uninterrupted power supply. Given the relevant role of energy in society, this work will study the consequences of the economic shutdown on the Iberian electricity market, and discuss the timeline of events, the macroeconomic outlook, the financial status of the major electric utility companies (prior to being hit by the COVID-19 health pandemic), the changes in load profile, the generation mix and, finally, the electricity market spot prices.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energy Reportsarrow_drop_down
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    P.M.R. Bento in OpenAIRE
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    J.A.N. Pombo in OpenAIRE

    The ongoing COVID-19 pandemic has established itself has one of the biggest health crises facing humanity. Countries all around the world were forced to adopt unprecedented restrictive measures in order to halt the spread of the virus and safeguard public health. These measures have profoundly changed the way of life and severely affected practically all sectors of activity, with major demand and supply shocks, leading to one of the largest recessions in world history. An essential pillar to the proper functioning of modern societies is energy security, particularly electricity security, which guarantees a reliable and efficient supply of electricity. Energy distributors and utility companies remained operational during mandatory stay-at-home orders, to ensure an uninterrupted power supply. Given the relevant role of energy in society, this work will study the consequences of the economic shutdown on the Iberian electricity market, and discuss the timeline of events, the macroeconomic outlook, the financial status of the major electric utility companies (prior to being hit by the COVID-19 health pandemic), the changes in load profile, the generation mix and, finally, the electricity market spot prices.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energy Reportsarrow_drop_down
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