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description Publicationkeyboard_double_arrow_right Article 2024Publisher:MDPI AG Authors:Bianca Magalhães;
Bianca Magalhães
Bianca Magalhães in OpenAIREPedro Bento;
Pedro Bento
Pedro Bento in OpenAIREJosé Pombo;
José Pombo
José Pombo in OpenAIREMaria do Rosário Calado;
+1 AuthorsMaria do Rosário Calado
Maria do Rosário Calado in OpenAIREBianca Magalhães;
Bianca Magalhães
Bianca Magalhães in OpenAIREPedro Bento;
Pedro Bento
Pedro Bento in OpenAIREJosé Pombo;
José Pombo
José Pombo in OpenAIREMaria do Rosário Calado;
Maria do Rosário Calado
Maria do Rosário Calado in OpenAIRESílvio Mariano;
Sílvio Mariano
Sílvio Mariano in OpenAIREdoi: 10.3390/en17081926
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.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en17081926&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 9 citations 9 popularity Average influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en17081926&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2024Publisher:MDPI AG Authors:Bianca Magalhães;
Bianca Magalhães
Bianca Magalhães in OpenAIREPedro Bento;
Pedro Bento
Pedro Bento in OpenAIREJosé Pombo;
José Pombo
José Pombo in OpenAIREMaria do Rosário Calado;
+1 AuthorsMaria do Rosário Calado
Maria do Rosário Calado in OpenAIREBianca Magalhães;
Bianca Magalhães
Bianca Magalhães in OpenAIREPedro Bento;
Pedro Bento
Pedro Bento in OpenAIREJosé Pombo;
José Pombo
José Pombo in OpenAIREMaria do Rosário Calado;
Maria do Rosário Calado
Maria do Rosário Calado in OpenAIRESílvio Mariano;
Sílvio Mariano
Sílvio Mariano in OpenAIREdoi: 10.3390/en17081926
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.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en17081926&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 9 citations 9 popularity Average influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en17081926&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Elsevier BV Funded by:FCT | SFRH/BD/140371/2018FCT| SFRH/BD/140371/2018Authors: M.R.A. Calado;José Pombo;
R.P.G. Mendes;José Pombo
José Pombo in OpenAIREP.M.R. Bento;
+1 AuthorsP.M.R. Bento
P.M.R. Bento in OpenAIREM.R.A. Calado;José Pombo;
R.P.G. Mendes;José Pombo
José Pombo in OpenAIREP.M.R. Bento;
Sílvio Mariano;P.M.R. Bento
P.M.R. Bento in OpenAIREAbstract 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.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.oceaneng.2020.108372&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu80 citations 80 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.oceaneng.2020.108372&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Elsevier BV Funded by:FCT | SFRH/BD/140371/2018FCT| SFRH/BD/140371/2018Authors: M.R.A. Calado;José Pombo;
R.P.G. Mendes;José Pombo
José Pombo in OpenAIREP.M.R. Bento;
+1 AuthorsP.M.R. Bento
P.M.R. Bento in OpenAIREM.R.A. Calado;José Pombo;
R.P.G. Mendes;José Pombo
José Pombo in OpenAIREP.M.R. Bento;
Sílvio Mariano;P.M.R. Bento
P.M.R. Bento in OpenAIREAbstract 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.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.oceaneng.2020.108372&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu80 citations 80 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.oceaneng.2020.108372&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 PortugalPublisher:Elsevier BV Authors:Magalhães, Bianca G.;
Magalhães, Bianca G.
Magalhães, Bianca G. in OpenAIREBento, Pedro M. R.;
Bento, Pedro M. R.
Bento, Pedro M. R. in OpenAIREPombo, José;
Pombo, José
Pombo, José in OpenAIRECalado, M. do Rosário;
+1 AuthorsCalado, M. do Rosário
Calado, M. do Rosário in OpenAIREMagalhães, Bianca G.;
Magalhães, Bianca G.
Magalhães, Bianca G. in OpenAIREBento, Pedro M. R.;
Bento, Pedro M. R.
Bento, Pedro M. R. in OpenAIREPombo, José;
Pombo, José
Pombo, José in OpenAIRECalado, M. do Rosário;
Calado, M. do Rosário
Calado, M. do Rosário in OpenAIREMariano, Sílvio J. P S.;
Mariano, Sílvio J. P S.
Mariano, Sílvio J. P S. in OpenAIREhandle: 10400.6/13894
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.
Universidade da Beir... arrow_drop_down Universidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2024License: CC BYFull-Text: http://hdl.handle.net/10400.6/13894Data sources: Bielefeld Academic Search Engine (BASE)Expert Systems with ApplicationsArticle . 2023 . Peer-reviewedLicense: CC BYData sources: CrossrefuBibliorum Repositorio Digital da UBIArticle . 2023License: CC BYData sources: uBibliorum Repositorio Digital da UBIadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.eswa.2023.120059&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 12 citations 12 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
visibility 94visibility views 94 download downloads 24 Powered bymore_vert Universidade da Beir... arrow_drop_down Universidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2024License: CC BYFull-Text: http://hdl.handle.net/10400.6/13894Data sources: Bielefeld Academic Search Engine (BASE)Expert Systems with ApplicationsArticle . 2023 . Peer-reviewedLicense: CC BYData sources: CrossrefuBibliorum Repositorio Digital da UBIArticle . 2023License: CC BYData sources: uBibliorum Repositorio Digital da UBIadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.eswa.2023.120059&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 PortugalPublisher:Elsevier BV Authors:Magalhães, Bianca G.;
Magalhães, Bianca G.
Magalhães, Bianca G. in OpenAIREBento, Pedro M. R.;
Bento, Pedro M. R.
Bento, Pedro M. R. in OpenAIREPombo, José;
Pombo, José
Pombo, José in OpenAIRECalado, M. do Rosário;
+1 AuthorsCalado, M. do Rosário
Calado, M. do Rosário in OpenAIREMagalhães, Bianca G.;
Magalhães, Bianca G.
Magalhães, Bianca G. in OpenAIREBento, Pedro M. R.;
Bento, Pedro M. R.
Bento, Pedro M. R. in OpenAIREPombo, José;
Pombo, José
Pombo, José in OpenAIRECalado, M. do Rosário;
Calado, M. do Rosário
Calado, M. do Rosário in OpenAIREMariano, Sílvio J. P S.;
Mariano, Sílvio J. P S.
Mariano, Sílvio J. P S. in OpenAIREhandle: 10400.6/13894
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.
Universidade da Beir... arrow_drop_down Universidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2024License: CC BYFull-Text: http://hdl.handle.net/10400.6/13894Data sources: Bielefeld Academic Search Engine (BASE)Expert Systems with ApplicationsArticle . 2023 . Peer-reviewedLicense: CC BYData sources: CrossrefuBibliorum Repositorio Digital da UBIArticle . 2023License: CC BYData sources: uBibliorum Repositorio Digital da UBIadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.eswa.2023.120059&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 12 citations 12 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
visibility 94visibility views 94 download downloads 24 Powered bymore_vert Universidade da Beir... arrow_drop_down Universidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2024License: CC BYFull-Text: http://hdl.handle.net/10400.6/13894Data sources: Bielefeld Academic Search Engine (BASE)Expert Systems with ApplicationsArticle . 2023 . Peer-reviewedLicense: CC BYData sources: CrossrefuBibliorum Repositorio Digital da UBIArticle . 2023License: CC BYData sources: uBibliorum Repositorio Digital da UBIadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.eswa.2023.120059&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019 PortugalPublisher:Elsevier BV Funded by:FCT | SFRH/BD/140304/2018FCT| SFRH/BD/140304/2018Authors:Nunes, H.G.G.;
Nunes, H.G.G.
Nunes, H.G.G. in OpenAIREPombo, José Álvaro Nunes;
Pombo, José Álvaro Nunes
Pombo, José Álvaro Nunes in OpenAIREBento, P.M.R.;
Bento, P.M.R.
Bento, P.M.R. in OpenAIREMariano, S.;
+1 AuthorsMariano, S.
Mariano, S. in OpenAIRENunes, H.G.G.;
Nunes, H.G.G.
Nunes, H.G.G. in OpenAIREPombo, José Álvaro Nunes;
Pombo, José Álvaro Nunes
Pombo, José Álvaro Nunes in OpenAIREBento, P.M.R.;
Bento, P.M.R.
Bento, P.M.R. in OpenAIREMariano, S.;
Mariano, S.
Mariano, S. in OpenAIRECalado, M. Do Rosário;
Calado, M. Do Rosário
Calado, M. Do Rosário in OpenAIREhandle: 10400.6/7051
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.
uBibliorum Repositor... arrow_drop_down uBibliorum Repositorio Digital da UBIArticle . 2019Data sources: uBibliorum Repositorio Digital da UBIEnergy Conversion and ManagementArticle . 2019 . Peer-reviewedLicense: Elsevier TDMData sources: CrossrefUniversidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.enconman.2019.02.003&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu102 citations 102 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
visibility 34visibility views 34 download downloads 18 Powered bymore_vert uBibliorum Repositor... arrow_drop_down uBibliorum Repositorio Digital da UBIArticle . 2019Data sources: uBibliorum Repositorio Digital da UBIEnergy Conversion and ManagementArticle . 2019 . Peer-reviewedLicense: Elsevier TDMData sources: CrossrefUniversidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.enconman.2019.02.003&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019 PortugalPublisher:Elsevier BV Funded by:FCT | SFRH/BD/140304/2018FCT| SFRH/BD/140304/2018Authors:Nunes, H.G.G.;
Nunes, H.G.G.
Nunes, H.G.G. in OpenAIREPombo, José Álvaro Nunes;
Pombo, José Álvaro Nunes
Pombo, José Álvaro Nunes in OpenAIREBento, P.M.R.;
Bento, P.M.R.
Bento, P.M.R. in OpenAIREMariano, S.;
+1 AuthorsMariano, S.
Mariano, S. in OpenAIRENunes, H.G.G.;
Nunes, H.G.G.
Nunes, H.G.G. in OpenAIREPombo, José Álvaro Nunes;
Pombo, José Álvaro Nunes
Pombo, José Álvaro Nunes in OpenAIREBento, P.M.R.;
Bento, P.M.R.
Bento, P.M.R. in OpenAIREMariano, S.;
Mariano, S.
Mariano, S. in OpenAIRECalado, M. Do Rosário;
Calado, M. Do Rosário
Calado, M. Do Rosário in OpenAIREhandle: 10400.6/7051
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.
uBibliorum Repositor... arrow_drop_down uBibliorum Repositorio Digital da UBIArticle . 2019Data sources: uBibliorum Repositorio Digital da UBIEnergy Conversion and ManagementArticle . 2019 . Peer-reviewedLicense: Elsevier TDMData sources: CrossrefUniversidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.enconman.2019.02.003&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu102 citations 102 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
visibility 34visibility views 34 download downloads 18 Powered bymore_vert uBibliorum Repositor... arrow_drop_down uBibliorum Repositorio Digital da UBIArticle . 2019Data sources: uBibliorum Repositorio Digital da UBIEnergy Conversion and ManagementArticle . 2019 . Peer-reviewedLicense: Elsevier TDMData sources: CrossrefUniversidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.enconman.2019.02.003&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Institution of Engineering and Technology (IET) Authors:Pedro Bento;
Pedro Bento
Pedro Bento in OpenAIREJosé Pombo;
José Pombo
José Pombo in OpenAIREMaria do Rosário Calado;
Maria do Rosário Calado
Maria do Rosário Calado in OpenAIRESílvio Mariano;
Sílvio Mariano
Sílvio Mariano in OpenAIREdoi: 10.1049/rpg2.12258
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.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1049/rpg2.12258&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 14 citations 14 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1049/rpg2.12258&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Institution of Engineering and Technology (IET) Authors:Pedro Bento;
Pedro Bento
Pedro Bento in OpenAIREJosé Pombo;
José Pombo
José Pombo in OpenAIREMaria do Rosário Calado;
Maria do Rosário Calado
Maria do Rosário Calado in OpenAIRESílvio Mariano;
Sílvio Mariano
Sílvio Mariano in OpenAIREdoi: 10.1049/rpg2.12258
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.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1049/rpg2.12258&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 14 citations 14 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1049/rpg2.12258&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal , Other literature type 2021Publisher:MDPI AG Funded by:FCT | SFRH/BD/140371/2018FCT| SFRH/BD/140371/2018Authors:Pedro M. R. Bento;
Pedro M. R. Bento
Pedro M. R. Bento in OpenAIREJose A. N. Pombo;
Jose A. N. Pombo
Jose A. N. Pombo in OpenAIREMaria R. A. Calado;
Maria R. A. Calado
Maria R. A. Calado in OpenAIRESilvio J. P. S. Mariano;
Silvio J. P. S. Mariano
Silvio J. P. S. Mariano in OpenAIREdoi: 10.3390/en14217378
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.
Energies arrow_drop_down EnergiesOther literature type . 2021License: CC BYFull-Text: http://www.mdpi.com/1996-1073/14/21/7378/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en14217378&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 29 citations 29 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Energies arrow_drop_down EnergiesOther literature type . 2021License: CC BYFull-Text: http://www.mdpi.com/1996-1073/14/21/7378/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en14217378&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal , Other literature type 2021Publisher:MDPI AG Funded by:FCT | SFRH/BD/140371/2018FCT| SFRH/BD/140371/2018Authors:Pedro M. R. Bento;
Pedro M. R. Bento
Pedro M. R. Bento in OpenAIREJose A. N. Pombo;
Jose A. N. Pombo
Jose A. N. Pombo in OpenAIREMaria R. A. Calado;
Maria R. A. Calado
Maria R. A. Calado in OpenAIRESilvio J. P. S. Mariano;
Silvio J. P. S. Mariano
Silvio J. P. S. Mariano in OpenAIREdoi: 10.3390/en14217378
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.
Energies arrow_drop_down EnergiesOther literature type . 2021License: CC BYFull-Text: http://www.mdpi.com/1996-1073/14/21/7378/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en14217378&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 29 citations 29 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Energies arrow_drop_down EnergiesOther literature type . 2021License: CC BYFull-Text: http://www.mdpi.com/1996-1073/14/21/7378/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en14217378&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019 PortugalPublisher:Elsevier BV Funded by:FCT | SFRH/BD/140371/2018FCT| SFRH/BD/140371/2018Authors:Bento, P.M.R.;
Bento, P.M.R.
Bento, P.M.R. in OpenAIREPombo, José Álvaro Nunes;
Pombo, José Álvaro Nunes
Pombo, José Álvaro Nunes in OpenAIRECalado, M. Do Rosário;
Calado, M. Do Rosário
Calado, M. Do Rosário in OpenAIREMariano, S.;
Mariano, S.
Mariano, S. in OpenAIREhandle: 10400.6/7142
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.
uBibliorum Repositor... arrow_drop_down uBibliorum Repositorio Digital da UBIArticle . 2019Data sources: uBibliorum Repositorio Digital da UBIUniversidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.neucom.2019.05.030&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu69 citations 69 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
visibility 26visibility views 26 download downloads 10 Powered bymore_vert uBibliorum Repositor... arrow_drop_down uBibliorum Repositorio Digital da UBIArticle . 2019Data sources: uBibliorum Repositorio Digital da UBIUniversidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.neucom.2019.05.030&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019 PortugalPublisher:Elsevier BV Funded by:FCT | SFRH/BD/140371/2018FCT| SFRH/BD/140371/2018Authors:Bento, P.M.R.;
Bento, P.M.R.
Bento, P.M.R. in OpenAIREPombo, José Álvaro Nunes;
Pombo, José Álvaro Nunes
Pombo, José Álvaro Nunes in OpenAIRECalado, M. Do Rosário;
Calado, M. Do Rosário
Calado, M. Do Rosário in OpenAIREMariano, S.;
Mariano, S.
Mariano, S. in OpenAIREhandle: 10400.6/7142
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.
uBibliorum Repositor... arrow_drop_down uBibliorum Repositorio Digital da UBIArticle . 2019Data sources: uBibliorum Repositorio Digital da UBIUniversidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.neucom.2019.05.030&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu69 citations 69 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
visibility 26visibility views 26 download downloads 10 Powered bymore_vert uBibliorum Repositor... arrow_drop_down uBibliorum Repositorio Digital da UBIArticle . 2019Data sources: uBibliorum Repositorio Digital da UBIUniversidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.neucom.2019.05.030&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article 2018 PortugalPublisher:IEEE Authors:Bento, P.M.R.;
Bento, P.M.R.
Bento, P.M.R. in OpenAIRENunes, H.G.G.;
Nunes, H.G.G.
Nunes, H.G.G. in OpenAIREÁlvaro Nunes Pombo, José;
Álvaro Nunes Pombo, José
Álvaro Nunes Pombo, José in OpenAIREMariano, S.;
+1 AuthorsMariano, S.
Mariano, S. in OpenAIREBento, P.M.R.;
Bento, P.M.R.
Bento, P.M.R. in OpenAIRENunes, H.G.G.;
Nunes, H.G.G.
Nunes, H.G.G. in OpenAIREÁlvaro Nunes Pombo, José;
Álvaro Nunes Pombo, José
Álvaro Nunes Pombo, José in OpenAIREMariano, S.;
Mariano, S.
Mariano, S. in OpenAIRECalado, M. do Rosário;
Calado, M. do Rosário
Calado, M. do Rosário in OpenAIREhandle: 10400.6/8213
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.
uBibliorum Repositor... arrow_drop_down uBibliorum Repositorio Digital da UBIArticle . 2018Data sources: uBibliorum Repositorio Digital da UBIUniversidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/eeeic.2018.8493695&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu3 citations 3 popularity Top 10% influence Average impulse Average Powered by BIP!
visibility 34visibility views 34 download downloads 16 Powered bymore_vert uBibliorum Repositor... arrow_drop_down uBibliorum Repositorio Digital da UBIArticle . 2018Data sources: uBibliorum Repositorio Digital da UBIUniversidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/eeeic.2018.8493695&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article 2018 PortugalPublisher:IEEE Authors:Bento, P.M.R.;
Bento, P.M.R.
Bento, P.M.R. in OpenAIRENunes, H.G.G.;
Nunes, H.G.G.
Nunes, H.G.G. in OpenAIREÁlvaro Nunes Pombo, José;
Álvaro Nunes Pombo, José
Álvaro Nunes Pombo, José in OpenAIREMariano, S.;
+1 AuthorsMariano, S.
Mariano, S. in OpenAIREBento, P.M.R.;
Bento, P.M.R.
Bento, P.M.R. in OpenAIRENunes, H.G.G.;
Nunes, H.G.G.
Nunes, H.G.G. in OpenAIREÁlvaro Nunes Pombo, José;
Álvaro Nunes Pombo, José
Álvaro Nunes Pombo, José in OpenAIREMariano, S.;
Mariano, S.
Mariano, S. in OpenAIRECalado, M. do Rosário;
Calado, M. do Rosário
Calado, M. do Rosário in OpenAIREhandle: 10400.6/8213
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.
uBibliorum Repositor... arrow_drop_down uBibliorum Repositorio Digital da UBIArticle . 2018Data sources: uBibliorum Repositorio Digital da UBIUniversidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/eeeic.2018.8493695&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu3 citations 3 popularity Top 10% influence Average impulse Average Powered by BIP!
visibility 34visibility views 34 download downloads 16 Powered bymore_vert uBibliorum Repositor... arrow_drop_down uBibliorum Repositorio Digital da UBIArticle . 2018Data sources: uBibliorum Repositorio Digital da UBIUniversidade da Beira Interior: Ubi Thesis - Conhecimento OnlineArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/eeeic.2018.8493695&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2022Publisher:IEEE Authors:Pedro M.R. Bento;
Pedro M.R. Bento
Pedro M.R. Bento in OpenAIREJose A.N. Pombo;
Jose A.N. Pombo
Jose A.N. Pombo in OpenAIRESilvio J.P.S. Mariano;
Maria R.A. Calado;Silvio J.P.S. Mariano
Silvio J.P.S. Mariano in OpenAIREhttps://doi.org/10.1... arrow_drop_down https://doi.org/10.1109/eeeic/...Conference object . 2022 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/eeeic/icpseurope54979.2022.9854690&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu3 citations 3 popularity Top 10% influence Average impulse Average Powered by BIP!
more_vert https://doi.org/10.1... arrow_drop_down https://doi.org/10.1109/eeeic/...Conference object . 2022 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/eeeic/icpseurope54979.2022.9854690&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2022Publisher:IEEE Authors:Pedro M.R. Bento;
Pedro M.R. Bento
Pedro M.R. Bento in OpenAIREJose A.N. Pombo;
Jose A.N. Pombo
Jose A.N. Pombo in OpenAIRESilvio J.P.S. Mariano;
Maria R.A. Calado;Silvio J.P.S. Mariano
Silvio J.P.S. Mariano in OpenAIREhttps://doi.org/10.1... arrow_drop_down https://doi.org/10.1109/eeeic/...Conference object . 2022 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/eeeic/icpseurope54979.2022.9854690&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu3 citations 3 popularity Top 10% influence Average impulse Average Powered by BIP!
more_vert https://doi.org/10.1... arrow_drop_down https://doi.org/10.1109/eeeic/...Conference object . 2022 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/eeeic/icpseurope54979.2022.9854690&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Journal 2021Publisher:Elsevier BV Funded by:FCT | IT, FCT | SFRH/BD/140371/2018FCT| IT ,FCT| SFRH/BD/140371/2018Authors:P.M.R. Bento;
P.M.R. Bento
P.M.R. Bento in OpenAIRES.J.P.S. Mariano;
S.J.P.S. Mariano
S.J.P.S. Mariano in OpenAIREM.R.A. Calado;
M.R.A. Calado
M.R.A. Calado in OpenAIREJ.A.N. Pombo;
J.A.N. Pombo
J.A.N. Pombo in OpenAIREThe 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.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egyr.2021.06.058&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 29 citations 29 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egyr.2021.06.058&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Journal 2021Publisher:Elsevier BV Funded by:FCT | IT, FCT | SFRH/BD/140371/2018FCT| IT ,FCT| SFRH/BD/140371/2018Authors:P.M.R. Bento;
P.M.R. Bento
P.M.R. Bento in OpenAIRES.J.P.S. Mariano;
S.J.P.S. Mariano
S.J.P.S. Mariano in OpenAIREM.R.A. Calado;
M.R.A. Calado
M.R.A. Calado in OpenAIREJ.A.N. Pombo;
J.A.N. Pombo
J.A.N. Pombo in OpenAIREThe 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.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egyr.2021.06.058&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 29 citations 29 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egyr.2021.06.058&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu