Publicação
Automating machine learning pipelines to reduce cost in renewable energy production
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Xufre, Patrícia | |
| dc.contributor.advisor | Reder, Maik | |
| dc.contributor.author | Dethlefs, Frederik Paul | |
| dc.date.accessioned | 2022-01-17T09:18:11Z | |
| dc.date.available | 2024-05-21T00:30:34Z | |
| dc.date.issued | 2021-06-02 | |
| dc.date.submitted | 2021-05-21 | |
| dc.description.abstract | Despite the strong world wide growth of the wind power industry, cost-effectiveness re-mains crucial for turbine operators. Un expected wind turbine failures contribute a high share to the total cost for operation & maintenance. By analysing failure data of over 1800 wind turbines, this study identifies the most cost contributing on components and evaluates the possibility of cost reduction with the application of predictive maintenance strategies. The results suggest a high potential of cost savings for maintenance and an even higher potential for down time cost. Further more, this work includes a practical example of ananomaly detection algorithm as a tool for wind turbine failure prediction. | pt_PT |
| dc.identifier.tid | 202770648 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/130979 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Wind energy | pt_PT |
| dc.subject | Predictive maintenance | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Cost analysis | pt_PT |
| dc.title | Automating machine learning pipelines to reduce cost in renewable energy production | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics | pt_PT |
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