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  • Energy Research
  • 2025-2025
  • 13. Climate action
  • 11. Sustainability
  • CN
  • GB

  • 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: orcid Cuartas, J;
    Cuartas, J
    ORCID
    Harvested from ORCID Public Data File

    Cuartas, J in OpenAIRE
    Bhatia, A; Carter, D; Cluver, L; +9 Authors

    The climate crisis is the biggest threat to the health, development, and wellbeing of the current and future generations. While there is extensive evidence on the direct impacts of climate change on human livelihood, there is little evidence on how children and young people are affected, and even less discussion and evidence on how the climate crisis could affect violence against children.In this commentary, we review selected research to assess the links between the climate crisis and violence against children.We employ a social-ecological perspective as an overarching framework to organize findings from the literature and call attention to increased violence against children as a specific, yet under-examined, direct and indirect consequence of the climate crisis.Using such a perspective, we examine how the climate crisis exacerbates the risk of violence against children at the continually intersecting and interacting levels of society, community, family, and the individual levels. We propose increased risk of armed conflict, forced displacement, poverty, income inequality, disruptions in critical health and social services, and mental health problems as key mechanisms linking the climate crisis and heightened risk of violence against children. Furthermore, we posit that the climate crisis serves as a threat multiplier, compounding existing vulnerabilities and inequities within populations and having harsher consequences in settings, communities, households, and for children already experiencing adversities.We conclude with a call for urgent efforts from researchers, practitioners, and policymakers to further investigate the specific empirical links between the climate crisis and violence against children and to design, test, implement, fund, and scale evidence-based, rights-based, and child friendly prevention, support, and response strategies to address violence against children.

    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 Oxford University Re...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
    Child Abuse & Neglect
    Article . 2025 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
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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 Oxford University Re...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
      Child Abuse & Neglect
      Article . 2025 . 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: orcid Cuartas, J;
    Cuartas, J
    ORCID
    Harvested from ORCID Public Data File

    Cuartas, J in OpenAIRE
    Bhatia, A; Carter, D; Cluver, L; +9 Authors

    The climate crisis is the biggest threat to the health, development, and wellbeing of the current and future generations. While there is extensive evidence on the direct impacts of climate change on human livelihood, there is little evidence on how children and young people are affected, and even less discussion and evidence on how the climate crisis could affect violence against children.In this commentary, we review selected research to assess the links between the climate crisis and violence against children.We employ a social-ecological perspective as an overarching framework to organize findings from the literature and call attention to increased violence against children as a specific, yet under-examined, direct and indirect consequence of the climate crisis.Using such a perspective, we examine how the climate crisis exacerbates the risk of violence against children at the continually intersecting and interacting levels of society, community, family, and the individual levels. We propose increased risk of armed conflict, forced displacement, poverty, income inequality, disruptions in critical health and social services, and mental health problems as key mechanisms linking the climate crisis and heightened risk of violence against children. Furthermore, we posit that the climate crisis serves as a threat multiplier, compounding existing vulnerabilities and inequities within populations and having harsher consequences in settings, communities, households, and for children already experiencing adversities.We conclude with a call for urgent efforts from researchers, practitioners, and policymakers to further investigate the specific empirical links between the climate crisis and violence against children and to design, test, implement, fund, and scale evidence-based, rights-based, and child friendly prevention, support, and response strategies to address violence against children.

    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 Oxford University Re...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
    Child Abuse & Neglect
    Article . 2025 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
    addClaim
    4
    citations4
    popularityAverage
    influenceAverage
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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 Oxford University Re...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
      Child Abuse & Neglect
      Article . 2025 . 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: Xuan He; orcid bw Danny H.K. Tsang;
    Danny H.K. Tsang
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Danny H.K. Tsang in OpenAIRE
    orcid Yize Chen;
    Yize Chen
    ORCID
    Harvested from ORCID Public Data File

    Yize Chen in OpenAIRE

    Global climate challenge is demanding urgent actions for decarbonization, while electric power systems take the major roles in clean energy transition. Due to the existence of spatially and temporally dispersed renewable energy resources and the uneven distribution of carbon emission intensity throughout the grid, it is worth investigating future load planning and demand management to offset those generations with higher carbon emission rates. Such techniques include inter-region utilization of geographically shiftable resources and stochastic renewable energy. For instance, data center is considered to be a major carbon emission producer in the future due to increasing information load, while it holds the capability of geographical load balancing. In this paper, we propose a novel planning and operation model minimizing the system-level carbon emissions via sitting and operating geographically shiftable resources. This model decides the optimal locations for shiftable resources expansion along with power dispatch schedule. To accommodate future system operation patterns and a wide range of operating conditions, we incorporate 20-year fine-grained load and renewables scenarios for grid simulations of realistic sizes (e.g., up to 1888 buses). To tackle the computational challenges coming from the combinatorial nature of such large-scale planning problem, we develop a customized Monte Carlo Tree Search (MCTS) method, which can find reasonable solutions satisfying solution time limits. Besides, MCTS enables flexible time window settings and offline solution adjustments. Extensive simulations validate that our planning model can reduce more than 10\% carbon emission across all setups. Compared to off-the-shelf optimization solvers such as Gurobi, our method achieves up to 8.1X acceleration while the solution gaps are less than 1.5\% in large-scale cases. Accepted at IEEE Transactions on Power Systems

    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/ https://dx.doi.org/1...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/
    https://dx.doi.org/10.48550/ar...
    Article . 2023
    License: CC BY
    Data sources: Datacite
    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/
    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
    IEEE Transactions on Power Systems
    Article . 2025 . Peer-reviewed
    License: IEEE Copyright
    Data sources: Crossref
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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/ https://dx.doi.org/1...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/
      https://dx.doi.org/10.48550/ar...
      Article . 2023
      License: CC BY
      Data sources: Datacite
      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/
      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
      IEEE Transactions on Power Systems
      Article . 2025 . Peer-reviewed
      License: IEEE Copyright
      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: Xuan He; orcid bw Danny H.K. Tsang;
    Danny H.K. Tsang
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Danny H.K. Tsang in OpenAIRE
    orcid Yize Chen;
    Yize Chen
    ORCID
    Harvested from ORCID Public Data File

    Yize Chen in OpenAIRE

    Global climate challenge is demanding urgent actions for decarbonization, while electric power systems take the major roles in clean energy transition. Due to the existence of spatially and temporally dispersed renewable energy resources and the uneven distribution of carbon emission intensity throughout the grid, it is worth investigating future load planning and demand management to offset those generations with higher carbon emission rates. Such techniques include inter-region utilization of geographically shiftable resources and stochastic renewable energy. For instance, data center is considered to be a major carbon emission producer in the future due to increasing information load, while it holds the capability of geographical load balancing. In this paper, we propose a novel planning and operation model minimizing the system-level carbon emissions via sitting and operating geographically shiftable resources. This model decides the optimal locations for shiftable resources expansion along with power dispatch schedule. To accommodate future system operation patterns and a wide range of operating conditions, we incorporate 20-year fine-grained load and renewables scenarios for grid simulations of realistic sizes (e.g., up to 1888 buses). To tackle the computational challenges coming from the combinatorial nature of such large-scale planning problem, we develop a customized Monte Carlo Tree Search (MCTS) method, which can find reasonable solutions satisfying solution time limits. Besides, MCTS enables flexible time window settings and offline solution adjustments. Extensive simulations validate that our planning model can reduce more than 10\% carbon emission across all setups. Compared to off-the-shelf optimization solvers such as Gurobi, our method achieves up to 8.1X acceleration while the solution gaps are less than 1.5\% in large-scale cases. Accepted at IEEE Transactions on Power Systems

    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/ https://dx.doi.org/1...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/
    https://dx.doi.org/10.48550/ar...
    Article . 2023
    License: CC BY
    Data sources: Datacite
    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/
    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
    IEEE Transactions on Power Systems
    Article . 2025 . Peer-reviewed
    License: IEEE Copyright
    Data sources: Crossref
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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/ https://dx.doi.org/1...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/
      https://dx.doi.org/10.48550/ar...
      Article . 2023
      License: CC BY
      Data sources: Datacite
      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/
      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
      IEEE Transactions on Power Systems
      Article . 2025 . Peer-reviewed
      License: IEEE Copyright
      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: Jiming Hu; Xiaoyan Han;

    In order to solve the problem of excessive burden of electricity and energy consumption in urban landscape buildings clusters, the study combined data mining algorithms to establish a prediction model for energy-saving renovation of urban landscape building clusters. Firstly, the energy demand and energy consumption of theurban landscape buildings complex were analysed, a mathematical model was established to predict the energy consumption of the building complex. Then, the prediction model of energy-saving retrofitting of building clusters was constructed by combining data mining techniques. The experimental results show that the change trend of total energy consumption is different under different single influencing factors of energy consumption. Among them, the lighting power density factor has the greatest influence on energy consumption, and its annual energy consumption change rate can reach about 0.35. Applying the prediction model to the energy consumption prediction of 15 urban single buildings, it was found that the total energy consumption of the buildings before the retrofit was much higher than that after the retrofit, and the energy-saving rate of the whole observed sample building group was as high as 18.5%, meanwhile, the highest energy-saving rate of the single buildings reached 30.1%.

    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/ Proceedings of the I...arrow_drop_down
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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/ Proceedings of the I...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/
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    Authors: Jiming Hu; Xiaoyan Han;

    In order to solve the problem of excessive burden of electricity and energy consumption in urban landscape buildings clusters, the study combined data mining algorithms to establish a prediction model for energy-saving renovation of urban landscape building clusters. Firstly, the energy demand and energy consumption of theurban landscape buildings complex were analysed, a mathematical model was established to predict the energy consumption of the building complex. Then, the prediction model of energy-saving retrofitting of building clusters was constructed by combining data mining techniques. The experimental results show that the change trend of total energy consumption is different under different single influencing factors of energy consumption. Among them, the lighting power density factor has the greatest influence on energy consumption, and its annual energy consumption change rate can reach about 0.35. Applying the prediction model to the energy consumption prediction of 15 urban single buildings, it was found that the total energy consumption of the buildings before the retrofit was much higher than that after the retrofit, and the energy-saving rate of the whole observed sample building group was as high as 18.5%, meanwhile, the highest energy-saving rate of the single buildings reached 30.1%.

    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/ Proceedings of the I...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/
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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/ Proceedings of the I...arrow_drop_down
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    Authors: Syed Yousufuddin; Naseeb Khan; Muhammad Saleem;

    With the advent of employing bio-fuels along with the diesel in compression ignition engines the study of performance and emission characteristics have occupied the prominence, owing to diversified multi responses. As the limited information is available about the application of Taguchi based GTMA process to maximize the overall performance and emission characteristics of diesel engine, in the present work the investigation was carried out to maximize the overall utility by employing the Taguchi based GTMA process. By following the user preference rating, weights for the response characteristics namely brake thermal efficiency, brake specific fuel consumption, carbon monoxide and oxides of nitrogen were calculated using graph theory and matrix approach (GTMA). The parameter hydrogen induction played a major role to an extent of 78.62% while Injection opening pressure playing a minor role with a contribution of 7.06%. The optimal parameters condition was at mid-level of the governing parameters namely IOP, CR and volume of hydrogen inducted. The predicted results were within 95% of confidence interval of the optimal values. Therefore, the hydrogen inductance into the cylinder not only improving the performance but also minimizing the emission characteristics.

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    Authors: Syed Yousufuddin; Naseeb Khan; Muhammad Saleem;

    With the advent of employing bio-fuels along with the diesel in compression ignition engines the study of performance and emission characteristics have occupied the prominence, owing to diversified multi responses. As the limited information is available about the application of Taguchi based GTMA process to maximize the overall performance and emission characteristics of diesel engine, in the present work the investigation was carried out to maximize the overall utility by employing the Taguchi based GTMA process. By following the user preference rating, weights for the response characteristics namely brake thermal efficiency, brake specific fuel consumption, carbon monoxide and oxides of nitrogen were calculated using graph theory and matrix approach (GTMA). The parameter hydrogen induction played a major role to an extent of 78.62% while Injection opening pressure playing a minor role with a contribution of 7.06%. The optimal parameters condition was at mid-level of the governing parameters namely IOP, CR and volume of hydrogen inducted. The predicted results were within 95% of confidence interval of the optimal values. Therefore, the hydrogen inductance into the cylinder not only improving the performance but also minimizing the emission characteristics.

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    Authors: Guilong Peng; Senshan Sun; Zhenwei Xu; Juxin Du; +5 Authors

    Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.

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    International Journal of Heat and Mass Transfer
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    Authors: Guilong Peng; Senshan Sun; Zhenwei Xu; Juxin Du; +5 Authors

    Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.

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    Authors: orcid Christoph Bergmeir;
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    Abishek Sriramulu; +24 Authors

    Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.

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    Evgenii Genov in OpenAIRE
    Abishek Sriramulu; +24 Authors

    Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.

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    Authors: orcid Luis Badesa;
    Luis Badesa
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    orcid Carlos Matamala;
    Carlos Matamala
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    Carlos Matamala in OpenAIRE
    Goran Strbac;

    While the operating cost of electricity grids based on thermal generation was largely driven by the cost of fuel, as renewable penetration increases, ancillary services represent an increasingly large proportion of the running costs. Electric frequency is an important magnitude in highly renewable grids, as it becomes more volatile and therefore the cost related to maintaining it within safe bounds has significantly increased. So far, costs for frequency-containment ancillary services have been socialised in most countries, but it has become relevant to rethink this regulatory arrangement. In this paper, we discuss the issue of cost allocation for these services, highlighting the need to evolve towards a causation-based regulatory framework. We argue that parties responsible for creating the need for ancillary services should bear these costs. However, this would imply an important change in electricity market policy, therefore it is necessary to understand the impact on current and future investments on generation, as well as on electricity tariffs. Here we provide a mostly qualitative analysis of this issue, defining guidelines for practical implementation and further study. Published in journal Energy Policy

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    Authors: orcid Luis Badesa;
    Luis Badesa
    ORCID
    Harvested from ORCID Public Data File

    Luis Badesa in OpenAIRE
    orcid Carlos Matamala;
    Carlos Matamala
    ORCID
    Harvested from ORCID Public Data File

    Carlos Matamala in OpenAIRE
    Goran Strbac;

    While the operating cost of electricity grids based on thermal generation was largely driven by the cost of fuel, as renewable penetration increases, ancillary services represent an increasingly large proportion of the running costs. Electric frequency is an important magnitude in highly renewable grids, as it becomes more volatile and therefore the cost related to maintaining it within safe bounds has significantly increased. So far, costs for frequency-containment ancillary services have been socialised in most countries, but it has become relevant to rethink this regulatory arrangement. In this paper, we discuss the issue of cost allocation for these services, highlighting the need to evolve towards a causation-based regulatory framework. We argue that parties responsible for creating the need for ancillary services should bear these costs. However, this would imply an important change in electricity market policy, therefore it is necessary to understand the impact on current and future investments on generation, as well as on electricity tariffs. Here we provide a mostly qualitative analysis of this issue, defining guidelines for practical implementation and further study. Published in journal Energy Policy

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    Energy Policy
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  • Authors: Xinyu Wang; Yuancheng Li; Jiexuan Yuan; Hao Luo;

    Background: Under the "Dual Carbon" strategy, the new-type power system integrates various complex equipment, especially the addition of wind power, photovoltaic power generation, distributed energy storage and other systems, which not only brings clean and efficient energy to the power system, but also leads to potential risks in the power grid. Objective: Traditional methods for assessing the risks of power systems mainly focus on the characteristics of traditional power grids. With the emergence of new features in the new power system, these traditional assessment methods are no longer effective, which could affect the safe and stable operation of the power system. To address this issue, this article proposes a new vulnerability index system for power systems. It also introduces the analytic hierarchy based on Moody's order graph (MAHP) quantification analysis method, and develops the Tabular data risk assessment methodology (TabRAM) risk assessment model based on an improved Transformer method. Methods: MAHP is a comprehensive evaluation method combining Moody's Chart and Analytic Hierarchy (AHP), which has high comprehensiveness and operability. TabRAM uses Bayesian neural networks (BNN) to complete prior data fitting and model complex feature dependencies and potential causal mechanisms on tabular data. The network is trained using an improved Transformer, and after pre-training, it can approximate new prior probabilistic inference in a forward pass to achieve prediction and classification tasks for new datasets. Results: After multiple rounds of iterations and parameter adjustments, the model achieved the desired performance and was successfully applied to the risk assessment of the new power system. Conclusion: According to the experimental results, the proposed TabRAM model demonstrates superior performance in the field of risk assessment for the new-type power system, as compared to traditional deep learning and machine learning algorithms.

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  • Authors: Xinyu Wang; Yuancheng Li; Jiexuan Yuan; Hao Luo;

    Background: Under the "Dual Carbon" strategy, the new-type power system integrates various complex equipment, especially the addition of wind power, photovoltaic power generation, distributed energy storage and other systems, which not only brings clean and efficient energy to the power system, but also leads to potential risks in the power grid. Objective: Traditional methods for assessing the risks of power systems mainly focus on the characteristics of traditional power grids. With the emergence of new features in the new power system, these traditional assessment methods are no longer effective, which could affect the safe and stable operation of the power system. To address this issue, this article proposes a new vulnerability index system for power systems. It also introduces the analytic hierarchy based on Moody's order graph (MAHP) quantification analysis method, and develops the Tabular data risk assessment methodology (TabRAM) risk assessment model based on an improved Transformer method. Methods: MAHP is a comprehensive evaluation method combining Moody's Chart and Analytic Hierarchy (AHP), which has high comprehensiveness and operability. TabRAM uses Bayesian neural networks (BNN) to complete prior data fitting and model complex feature dependencies and potential causal mechanisms on tabular data. The network is trained using an improved Transformer, and after pre-training, it can approximate new prior probabilistic inference in a forward pass to achieve prediction and classification tasks for new datasets. Results: After multiple rounds of iterations and parameter adjustments, the model achieved the desired performance and was successfully applied to the risk assessment of the new power system. Conclusion: According to the experimental results, the proposed TabRAM model demonstrates superior performance in the field of risk assessment for the new-type power system, as compared to traditional deep learning and machine learning algorithms.

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