AI-driven approaches for optimizing the energy efficiency of integrated energy system

annif.suggestionsrenewable energy sources|electrical power networks|machine learning|energy efficiency|energy production (process industry)|energy systems|energy control|smart grids|emissions|optimisation|enen
annif.suggestionsrenewable energy sources|electrical power networks|machine learning|energy efficiency|energy production (process industry)|energy systems|energy control|smart grids|emissions|optimisation|enen
annif.suggestions.linkshttp://www.yso.fi/onto/yso/p20762|http://www.yso.fi/onto/yso/p7753|http://www.yso.fi/onto/yso/p21846|http://www.yso.fi/onto/yso/p8328|http://www.yso.fi/onto/yso/p2384|http://www.yso.fi/onto/yso/p22348|http://www.yso.fi/onto/yso/p2388|http://www.yso.fi/onto/yso/p29493|http://www.yso.fi/onto/yso/p437|http://www.yso.fi/onto/yso/p13477en
annif.suggestions.linkshttp://www.yso.fi/onto/yso/p20762|http://www.yso.fi/onto/yso/p7753|http://www.yso.fi/onto/yso/p21846|http://www.yso.fi/onto/yso/p8328|http://www.yso.fi/onto/yso/p2384|http://www.yso.fi/onto/yso/p22348|http://www.yso.fi/onto/yso/p2388|http://www.yso.fi/onto/yso/p29493|http://www.yso.fi/onto/yso/p437|http://www.yso.fi/onto/yso/p13477en
dc.contributor.authorThapa, Nitesh
dc.contributor.facultyfi=Tekniikan ja innovaatiojohtamisen yksikkö|en=School of Technology and Innovations|-
dc.contributor.organizationfi=Vaasan yliopisto|en=University of Vaasa|
dc.date.accessioned2022-06-03T12:08:26Z
dc.date.accessioned2025-06-25T17:33:27Z
dc.date.available2022-06-03T12:08:26Z
dc.date.issued2022-05-31
dc.description.abstractTo decarbonize the global energy system and replace the unidirectional architecture of existing grid networks, integrated and electrified energy systems are becoming more demanding. Energy integration is critical for renewable energy sources like wind, solar, and hydropower. However, there are still specific challenges to overcome, such as their high reliance on the weather and the complexity of their integrated operation. As a result, this research goes through the study of a new approach to energy service that has arisen in the shape of data-driven AI technologies, which hold tremendous promise for system improvement while maximizing energy efficiency and reducing carbon emissions. This research aims to evaluate the use of data-driven AI techniques in electrical integrated energy systems, focusing on energy integration, operation, and planning of multiple energy supplies and demand. Based on the formation point, the main research question is: "To what extent do AI algorithms contribute to attaining greater efficiency of integrated grid systems?". It also included a discussion on four key research areas of AI application: Energy and load prediction, fault prediction, AI-based technologies IoT used for smart monitoring grid system optimization such as energy storage, demand response, grid flexibility, and Business value creation. The study adopted a two-way approach that includes empirical research on energy industry expert interviews and a Likert scale survey among energy sector representatives from Finland, Norway, and Nepal. On the other hand, the theoretical part was from current energy industry optimization models and a review of publications linked to a given research issue. The research's key findings were AI's significant potential in electrically integrated energy systems, which concluded AI's implication as a better understanding of energy consumption patterns, highly effective and precise energy load and fault prediction, automated energy management, enhanced energy storage system, more excellent business value, a smart control center, smooth monitoring, tracking, and communication of energy networks. In addition, further research directions are prospects towards its technical characteristics on energy conversion.-
dc.format.bitstreamtrue
dc.format.extent103-
dc.identifier.olddbid16510
dc.identifier.oldhandle10024/14257
dc.identifier.urihttps://osuva.uwasa.fi/handle/11111/11575
dc.identifier.urnURN:NBN:fi-fe2022053140984-
dc.language.isoeng-
dc.rightsCC BY-ND 4.0-
dc.source.identifierhttps://osuva.uwasa.fi/handle/10024/14257
dc.subject.degreeprogrammeMaster's Programme in Industrial Systems Analytics-
dc.subject.disciplinefi=Sähkö- ja energiatekniikka|en=Electrical Engineering and Energy Technology|-
dc.subject.ysoenergy efficiency-
dc.subject.ysoenergy systems-
dc.subject.ysoenergy control-
dc.subject.ysorenewable energy sources-
dc.subject.ysoelectrical power networks-
dc.subject.ysomachine learning-
dc.subject.ysosmart grids-
dc.subject.ysooptimisation-
dc.titleAI-driven approaches for optimizing the energy efficiency of integrated energy system-
dc.type.ontasotfi=Pro gradu -tutkielma|en=Master's thesis|sv=Pro gradu -avhandling|-

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Master Thesis on AI-driven approaches for optimizing the energy efficiency of integrated energy system
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UniVaasa_2022_Thapa_Nitesh.pdf
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Adobe Portable Document Format
Description:
Master Thesis on AI-driven approaches for optimizing the energy efficiency of integrated energy system