AutoMoG 3D: Automated Data-Driven Model Generation of Multi-Energy Systems Using Hinging Hyperplanes
The optimal operation of multi-energy systems requires optimization models that are accurate and computationally efficient. In practice, models are mostly generated manually. However, manual model generation is time-consuming, and model quality depends on the expertise of the modeler. Thus, reliable...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-08-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2021.719658/full |
| _version_ | 1819115410387107840 |
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| author | Andreas Kämper Alexander Holtwerth Ludger Leenders Ludger Leenders André Bardow André Bardow André Bardow |
| author_facet | Andreas Kämper Alexander Holtwerth Ludger Leenders Ludger Leenders André Bardow André Bardow André Bardow |
| author_sort | Andreas Kämper |
| collection | DOAJ |
| description | The optimal operation of multi-energy systems requires optimization models that are accurate and computationally efficient. In practice, models are mostly generated manually. However, manual model generation is time-consuming, and model quality depends on the expertise of the modeler. Thus, reliable and automated model generation is highly desirable. Automated data-driven model generation seems promising due to the increasing availability of measurement data from cheap sensors and data storage. Here, we propose the method AutoMoG 3D (Automated Model Generation) to decrease the effort for data-driven generation of computationally efficient models while retaining high model quality. AutoMoG 3D automatically yields Mixed-Integer Linear Programming models of multi-energy systems enabling efficient operational optimization to global optimality using established solvers. For each component, AutoMoG 3D performs a piecewise-affine regression using hinging-hyperplane trees. Thereby, components can be modeled with an arbitrary number of independent variables. AutoMoG 3D iteratively increases the number of affine regions. Thereby, AutoMoG 3D balances the errors caused by each component in the overall model of the multi-energy system. AutoMoG 3D is applied to model a real-world pump system. Here, AutoMoG 3D drastically decreases the effort for data-driven model generation and provides an accurate and computationally efficient optimization model. |
| first_indexed | 2024-12-22T05:00:45Z |
| format | Article |
| id | doaj.art-25905a64aeec4082b6984756f398e405 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-22T05:00:45Z |
| publishDate | 2021-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-25905a64aeec4082b6984756f398e4052022-12-21T18:38:15ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2021-08-01910.3389/fenrg.2021.719658719658AutoMoG 3D: Automated Data-Driven Model Generation of Multi-Energy Systems Using Hinging HyperplanesAndreas Kämper0Alexander Holtwerth1Ludger Leenders2Ludger Leenders3André Bardow4André Bardow5André Bardow6Institute of Technical Thermodynamics, RWTH Aachen University, Aachen, GermanyInstitute of Technical Thermodynamics, RWTH Aachen University, Aachen, GermanyInstitute of Technical Thermodynamics, RWTH Aachen University, Aachen, GermanyEnergy and Process Systems Engineering, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, SwitzerlandInstitute of Technical Thermodynamics, RWTH Aachen University, Aachen, GermanyEnergy and Process Systems Engineering, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, SwitzerlandInstitute of Energy and Climate Research, Energy Systems Engineering (IEK-10), Forschungszentrum Jülich GmbH, Jülich, GermanyThe optimal operation of multi-energy systems requires optimization models that are accurate and computationally efficient. In practice, models are mostly generated manually. However, manual model generation is time-consuming, and model quality depends on the expertise of the modeler. Thus, reliable and automated model generation is highly desirable. Automated data-driven model generation seems promising due to the increasing availability of measurement data from cheap sensors and data storage. Here, we propose the method AutoMoG 3D (Automated Model Generation) to decrease the effort for data-driven generation of computationally efficient models while retaining high model quality. AutoMoG 3D automatically yields Mixed-Integer Linear Programming models of multi-energy systems enabling efficient operational optimization to global optimality using established solvers. For each component, AutoMoG 3D performs a piecewise-affine regression using hinging-hyperplane trees. Thereby, components can be modeled with an arbitrary number of independent variables. AutoMoG 3D iteratively increases the number of affine regions. Thereby, AutoMoG 3D balances the errors caused by each component in the overall model of the multi-energy system. AutoMoG 3D is applied to model a real-world pump system. Here, AutoMoG 3D drastically decreases the effort for data-driven model generation and provides an accurate and computationally efficient optimization model.https://www.frontiersin.org/articles/10.3389/fenrg.2021.719658/fulldata-driven modelingregression analysispiecewise affinemixed-integer linear programminghinging hyperplanes |
| spellingShingle | Andreas Kämper Alexander Holtwerth Ludger Leenders Ludger Leenders André Bardow André Bardow André Bardow AutoMoG 3D: Automated Data-Driven Model Generation of Multi-Energy Systems Using Hinging Hyperplanes Frontiers in Energy Research data-driven modeling regression analysis piecewise affine mixed-integer linear programming hinging hyperplanes |
| title | AutoMoG 3D: Automated Data-Driven Model Generation of Multi-Energy Systems Using Hinging Hyperplanes |
| title_full | AutoMoG 3D: Automated Data-Driven Model Generation of Multi-Energy Systems Using Hinging Hyperplanes |
| title_fullStr | AutoMoG 3D: Automated Data-Driven Model Generation of Multi-Energy Systems Using Hinging Hyperplanes |
| title_full_unstemmed | AutoMoG 3D: Automated Data-Driven Model Generation of Multi-Energy Systems Using Hinging Hyperplanes |
| title_short | AutoMoG 3D: Automated Data-Driven Model Generation of Multi-Energy Systems Using Hinging Hyperplanes |
| title_sort | automog 3d automated data driven model generation of multi energy systems using hinging hyperplanes |
| topic | data-driven modeling regression analysis piecewise affine mixed-integer linear programming hinging hyperplanes |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2021.719658/full |
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