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Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization
descriptionPublicationkeyboard_double_arrow_right Article 2023Publisher:Elsevier BV
Authors: Tianqi Xiao; Fengqi You;
doi: 10.1016/j.apenergy.2023.121165
doi: 10.1016/j.apenergy.2023.121165
Applied Energyarrow_drop_down
Applied Energy
Article . 2023 . Peer-reviewed
License: Elsevier TDM
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Applied Energy
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Sustainable energy management and control for Decarbonization of complex multi-zone buildings with renewable solar and geothermal energies using machine learning, robust optimization, and predictive control
descriptionPublicationkeyboard_double_arrow_right Article 2024Publisher:Elsevier BV
Authors: Wei-Han Chen; Fengqi You;
doi: 10.1016/j.apenergy.2024.123802
doi: 10.1016/j.apenergy.2024.123802
Applied Energyarrow_drop_down
Applied Energy
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Applied Energy
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An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment
descriptionPublicationkeyboard_double_arrow_right Article 2023Publisher:Elsevier BV
Authors: Guoqing Hu; Fengqi You;
doi: 10.1016/j.apenergy.2023.121450
doi: 10.1016/j.apenergy.2023.121450
Applied Energyarrow_drop_down
Applied Energy
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Applied Energy
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Distributionally robust optimization for planning and scheduling under uncertainty
descriptionPublicationkeyboard_double_arrow_right Article , Journal 2018Publisher:Elsevier BV
Authors: Fengqi You; Chao Shang;
doi: 10.1016/j.compchemeng.2017.12.002
doi: 10.1016/j.compchemeng.2017.12.002
Abstract Distributionally robust optimization (DRO) is an emerging and effective method to address the inexactness of probability distributions of uncertain parameters in decision-making under uncertainty. We propose an effective DRO framework for planning and scheduling under demand uncertainties. A novel data-driven approach is proposed to construct ambiguity sets based on principal component analysis and first-order deviation functions, which help excavating accurate and useful information from uncertainty data. Moreover, it leads to mixed-integer linear reformulations of planning and scheduling problems. To account for the multi-stage sequential decision-making structure in process operations, we further develop multi-stage DRO models and adopt affine decision rules to address the computational issue. Applications in industrial-scale process network planning and batch process scheduling demonstrate that, the proposed DRO approach can effectively leverage uncertainty data information, better hedge against distributional ambiguity, and yield more profits.
Computers & Chemical...arrow_drop_down
Computers & Chemical Engineering
Article . 2018 . Peer-reviewed
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Computers & Chemical Engineering
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The growing metaverse sector can reduce greenhouse gas emissions by 10 Gt CO2e in the united states by 2050
descriptionPublicationkeyboard_double_arrow_right Article 2023Publisher:Royal Society of Chemistry (RSC)
Authors: Ning Zhao; Fengqi You;
doi: 10.1039/d3ee00081h
doi: 10.1039/d3ee00081h
Metaverse sector growth supports energy conservation, boosts renewable energy penetration, lessens fossil fuel dependency, and reduces anthropogenic emissions, such as greenhouse gases and aerosol precursors, thereby aiding climate change mitigation.
Energy & Environment...arrow_drop_down
Energy & Environmental Science
Article . 2023 . Peer-reviewed
License: http://rsc.li/journals-terms-of-use#chorus
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Energy & Environment...arrow_drop_down
Energy & Environmental Science
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Bayesian optimization for battery electric vehicle charging station placement by agent-based demand simulation
descriptionPublicationkeyboard_double_arrow_right Article 2024Publisher:Elsevier BV
Authors: Yuechen Sophia Liu; Mohammad Tayarani; Fengqi You; H. Oliver Gao;
doi: 10.1016/j.apenergy.2024.123975
doi: 10.1016/j.apenergy.2024.123975
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Techno-Economic Feasibility and Spatial Analysis of Thermochemical Conversion Pathways for Regional Poultry Waste Valorization
descriptionPublicationkeyboard_double_arrow_right Article , Journal 2020Publisher:American Chemical Society (ACS)
Authors: Raaj R. Bora; Yanqiu Tao; Johannes Lehmann; Jefferson W. Tester; +2 Authors
Raaj R. Bora; Yanqiu Tao; Johannes Lehmann; Jefferson W. Tester; Ruth E. Richardson; Fengqi You;
doi: 10.1021/acssuschemeng.0c01229
doi: 10.1021/acssuschemeng.0c01229
This study examines prominent thermochemical conversion technologies, such as slow pyrolysis, fast pyrolysis, gasification and hydrothermal liquefaction, for treating poultry litter in New York Sta...
ACS Sustainable Chem...arrow_drop_down
ACS Sustainable Chemistry & Engineering
Article . 2020 . Peer-reviewed
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Deep Reinforcement Learning Based Unit Commitment Scheduling under Load and Wind Power Uncertainty
descriptionPublicationkeyboard_double_arrow_right Article 2023Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Authors: Akshay Ajagekar; Fengqi You;
doi: 10.1109/tste.2022.3226106
doi: 10.1109/tste.2022.3226106
IEEE Transactions on...arrow_drop_down
IEEE Transactions on Sustainable Energy
Article . 2023 . Peer-reviewed
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Waste Polypropylene Plastic Recycling toward Climate Change Mitigation and Circular Economy: Energy, Environmental, and Technoeconomic Perspectives
descriptionPublicationkeyboard_double_arrow_right Article , Journal 2020Publisher:American Chemical Society (ACS)
Authors: Raaj R. Bora; Ralph Wang; Fengqi You;
doi: 10.1021/acssuschemeng.0c06311
doi: 10.1021/acssuschemeng.0c06311
Chemical recycling has the potential to reduce the environmental impacts from waste plastics, mitigate climate change, and contribute to circular economy. This study compares the environmental and ...
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Sustainable building climate control with renewable energy sources using nonlinear model predictive control
descriptionPublicationkeyboard_double_arrow_right Article 2022Publisher:Elsevier BV
Authors: Wei-Han Chen; Fengqi You;
doi: 10.1016/j.rser.2022.112830
doi: 10.1016/j.rser.2022.112830
Renewable and Sustai...arrow_drop_down
Renewable and Sustainable Energy Reviews
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The following results are related to Energy Research. Are you interested to view more results? Visit OpenAIRE - Explore.

44 Research products

download

Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization
descriptionPublicationkeyboard_double_arrow_right Article 2023Publisher:Elsevier BV
Authors: Tianqi Xiao; Fengqi You;
doi: 10.1016/j.apenergy.2023.121165
doi: 10.1016/j.apenergy.2023.121165
Applied Energyarrow_drop_down
Applied Energy
Article . 2023 . Peer-reviewed
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Applied Energyarrow_drop_down
Applied Energy
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Sustainable energy management and control for Decarbonization of complex multi-zone buildings with renewable solar and geothermal energies using machine learning, robust optimization, and predictive control
descriptionPublicationkeyboard_double_arrow_right Article 2024Publisher:Elsevier BV
Authors: Wei-Han Chen; Fengqi You;
doi: 10.1016/j.apenergy.2024.123802
doi: 10.1016/j.apenergy.2024.123802
Applied Energyarrow_drop_down
Applied Energy
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An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment
descriptionPublicationkeyboard_double_arrow_right Article 2023Publisher:Elsevier BV
Authors: Guoqing Hu; Fengqi You;
doi: 10.1016/j.apenergy.2023.121450
doi: 10.1016/j.apenergy.2023.121450
Applied Energyarrow_drop_down
Applied Energy
Article . 2023 . Peer-reviewed
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Applied Energyarrow_drop_down
Applied Energy
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Distributionally robust optimization for planning and scheduling under uncertainty
descriptionPublicationkeyboard_double_arrow_right Article , Journal 2018Publisher:Elsevier BV
Authors: Fengqi You; Chao Shang;
doi: 10.1016/j.compchemeng.2017.12.002
doi: 10.1016/j.compchemeng.2017.12.002
Abstract Distributionally robust optimization (DRO) is an emerging and effective method to address the inexactness of probability distributions of uncertain parameters in decision-making under uncertainty. We propose an effective DRO framework for planning and scheduling under demand uncertainties. A novel data-driven approach is proposed to construct ambiguity sets based on principal component analysis and first-order deviation functions, which help excavating accurate and useful information from uncertainty data. Moreover, it leads to mixed-integer linear reformulations of planning and scheduling problems. To account for the multi-stage sequential decision-making structure in process operations, we further develop multi-stage DRO models and adopt affine decision rules to address the computational issue. Applications in industrial-scale process network planning and batch process scheduling demonstrate that, the proposed DRO approach can effectively leverage uncertainty data information, better hedge against distributional ambiguity, and yield more profits.
Computers & Chemical...arrow_drop_down
Computers & Chemical Engineering
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The growing metaverse sector can reduce greenhouse gas emissions by 10 Gt CO2e in the united states by 2050
descriptionPublicationkeyboard_double_arrow_right Article 2023Publisher:Royal Society of Chemistry (RSC)
Authors: Ning Zhao; Fengqi You;
doi: 10.1039/d3ee00081h
doi: 10.1039/d3ee00081h
Metaverse sector growth supports energy conservation, boosts renewable energy penetration, lessens fossil fuel dependency, and reduces anthropogenic emissions, such as greenhouse gases and aerosol precursors, thereby aiding climate change mitigation.
Energy & Environment...arrow_drop_down
Energy & Environmental Science
Article . 2023 . Peer-reviewed
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Energy & Environmental Science
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Bayesian optimization for battery electric vehicle charging station placement by agent-based demand simulation
descriptionPublicationkeyboard_double_arrow_right Article 2024Publisher:Elsevier BV
Authors: Yuechen Sophia Liu; Mohammad Tayarani; Fengqi You; H. Oliver Gao;
doi: 10.1016/j.apenergy.2024.123975
doi: 10.1016/j.apenergy.2024.123975
Applied Energyarrow_drop_down
Applied Energy
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Applied Energy
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Techno-Economic Feasibility and Spatial Analysis of Thermochemical Conversion Pathways for Regional Poultry Waste Valorization
descriptionPublicationkeyboard_double_arrow_right Article , Journal 2020Publisher:American Chemical Society (ACS)
Authors: Raaj R. Bora; Yanqiu Tao; Johannes Lehmann; Jefferson W. Tester; +2 Authors
Raaj R. Bora; Yanqiu Tao; Johannes Lehmann; Jefferson W. Tester; Ruth E. Richardson; Fengqi You;
doi: 10.1021/acssuschemeng.0c01229
doi: 10.1021/acssuschemeng.0c01229
This study examines prominent thermochemical conversion technologies, such as slow pyrolysis, fast pyrolysis, gasification and hydrothermal liquefaction, for treating poultry litter in New York Sta...
ACS Sustainable Chem...arrow_drop_down
ACS Sustainable Chemistry & Engineering
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Deep Reinforcement Learning Based Unit Commitment Scheduling under Load and Wind Power Uncertainty
descriptionPublicationkeyboard_double_arrow_right Article 2023Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Authors: Akshay Ajagekar; Fengqi You;
doi: 10.1109/tste.2022.3226106
doi: 10.1109/tste.2022.3226106
IEEE Transactions on...arrow_drop_down
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Waste Polypropylene Plastic Recycling toward Climate Change Mitigation and Circular Economy: Energy, Environmental, and Technoeconomic Perspectives
descriptionPublicationkeyboard_double_arrow_right Article , Journal 2020Publisher:American Chemical Society (ACS)
Authors: Raaj R. Bora; Ralph Wang; Fengqi You;
doi: 10.1021/acssuschemeng.0c06311
doi: 10.1021/acssuschemeng.0c06311
Chemical recycling has the potential to reduce the environmental impacts from waste plastics, mitigate climate change, and contribute to circular economy. This study compares the environmental and ...
ACS Sustainable Chem...arrow_drop_down
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Sustainable building climate control with renewable energy sources using nonlinear model predictive control
descriptionPublicationkeyboard_double_arrow_right Article 2022Publisher:Elsevier BV
Authors: Wei-Han Chen; Fengqi You;
doi: 10.1016/j.rser.2022.112830
doi: 10.1016/j.rser.2022.112830
Renewable and Sustai...arrow_drop_down
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Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization

Sustainable energy management and control for Decarbonization of complex multi-zone buildings with renewable solar and geothermal energies using machine learning, robust optimization, and predictive control

An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment

Distributionally robust optimization for planning and scheduling under uncertainty

The growing metaverse sector can reduce greenhouse gas emissions by 10 Gt CO2e in the united states by 2050

Bayesian optimization for battery electric vehicle charging station placement by agent-based demand simulation

Techno-Economic Feasibility and Spatial Analysis of Thermochemical Conversion Pathways for Regional Poultry Waste Valorization

Deep Reinforcement Learning Based Unit Commitment Scheduling under Load and Wind Power Uncertainty

Waste Polypropylene Plastic Recycling toward Climate Change Mitigation and Circular Economy: Energy, Environmental, and Technoeconomic Perspectives

Sustainable building climate control with renewable energy sources using nonlinear model predictive control

Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization

Sustainable energy management and control for Decarbonization of complex multi-zone buildings with renewable solar and geothermal energies using machine learning, robust optimization, and predictive control

An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment

Distributionally robust optimization for planning and scheduling under uncertainty

The growing metaverse sector can reduce greenhouse gas emissions by 10 Gt CO2e in the united states by 2050

Bayesian optimization for battery electric vehicle charging station placement by agent-based demand simulation

Techno-Economic Feasibility and Spatial Analysis of Thermochemical Conversion Pathways for Regional Poultry Waste Valorization

Deep Reinforcement Learning Based Unit Commitment Scheduling under Load and Wind Power Uncertainty

Waste Polypropylene Plastic Recycling toward Climate Change Mitigation and Circular Economy: Energy, Environmental, and Technoeconomic Perspectives

Sustainable building climate control with renewable energy sources using nonlinear model predictive control

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