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Design and Application of MGA Analysis Package: A Python-based clustering package using machine learning algorithms to analyze near optimal energy systems

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Title: Design and Application of MGA Analysis Package: A Python-based clustering package using machine learning algorithms to analyze near optimal energy systems
Authors: Khalegaonkar, Gaurav Ulhas (author)
Contributors: De Vries, Laurens (mentor), Correljé, A. (graduation committee), Lombardi, F. (graduation committee), Delft University of Technology (degree granting institution)
Publication Year: 2023
Collection: Delft University of Technology: Institutional Repository
Subject Terms: Energy system modeling, Energy System Analysis, Machine learning, Python Package development
Description: Achieving the goals of the Paris Agreement requires a significant transformation of current energy systems. The energy sector has hundreds of technologies and millions of actors working together to balance the system. Researchers are using computer-based models to understand the techno-economic impacts on the energy system due to changes in one or more energy system components. Modelling to generate alternative (MGA) is an energy system optimization method which generates hundreds of equally possible near-optimal energy system configurations. From the point of view of an analyst, it becomes difficult to provide in-depth analysis for future action due to the sheer amount of data generated by the MGA optimization method. In other words, while analyzing the results of the MGA-based model, one of its strengths becomes its weakness. This thesis aims to design a clustering-based analysis method and Python package (MGA analysis package) based on the designed method to analyze the results of the MGA based optimization model using machine learning algorithms. The package is developed for energy analysts and users of the MGA optimization method to simplify the analysis of results. MGA analysis package clusters the solution space and identifies representative solutions for each cluster. Along with that, it provides a regional equality index to assess the distribution of energy infrastructure. An interactive dashboard is designed to use the MGA analysis package with three to four user inputs and provides information graphically and in text format. The MGA analysis package is hosted on the git-hub (https://edu.nl/gmarp) for applications and further development. ; Computer Science
Document Type: master thesis
Language: English
Availability: http://resolver.tudelft.nl/uuid:0e718808-4e38-441e-a65e-b4fff6a3e245
Rights: © 2023 Gaurav Ulhas Khalegaonkar
Accession Number: edsbas.FD84A3C2
Database: BASE
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  Data: Design and Application of MGA Analysis Package: A Python-based clustering package using machine learning algorithms to analyze near optimal energy systems
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  Group: Au
  Data: De Vries, Laurens (mentor)<br />Correljé, A. (graduation committee)<br />Lombardi, F. (graduation committee)<br />Delft University of Technology (degree granting institution)
– Name: DatePubCY
  Label: Publication Year
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  Data: 2023
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  Data: Delft University of Technology: Institutional Repository
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  Data: <searchLink fieldCode="DE" term="%22Energy+system+modeling%22">Energy system modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+System+Analysis%22">Energy System Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Python+Package+development%22">Python Package development</searchLink>
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  Data: Achieving the goals of the Paris Agreement requires a significant transformation of current energy systems. The energy sector has hundreds of technologies and millions of actors working together to balance the system. Researchers are using computer-based models to understand the techno-economic impacts on the energy system due to changes in one or more energy system components. Modelling to generate alternative (MGA) is an energy system optimization method which generates hundreds of equally possible near-optimal energy system configurations. From the point of view of an analyst, it becomes difficult to provide in-depth analysis for future action due to the sheer amount of data generated by the MGA optimization method. In other words, while analyzing the results of the MGA-based model, one of its strengths becomes its weakness. This thesis aims to design a clustering-based analysis method and Python package (MGA analysis package) based on the designed method to analyze the results of the MGA based optimization model using machine learning algorithms. The package is developed for energy analysts and users of the MGA optimization method to simplify the analysis of results. MGA analysis package clusters the solution space and identifies representative solutions for each cluster. Along with that, it provides a regional equality index to assess the distribution of energy infrastructure. An interactive dashboard is designed to use the MGA analysis package with three to four user inputs and provides information graphically and in text format. The MGA analysis package is hosted on the git-hub (https://edu.nl/gmarp) for applications and further development. ; Computer Science
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  Data: © 2023 Gaurav Ulhas Khalegaonkar
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      – Text: English
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      – SubjectFull: Energy system modeling
        Type: general
      – SubjectFull: Energy System Analysis
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Python Package development
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      – TitleFull: Design and Application of MGA Analysis Package: A Python-based clustering package using machine learning algorithms to analyze near optimal energy systems
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