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Greenhouse Climate Optimization using Weather Forecasts and Machine Learning
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

It is difficult for a small scaled local farmer to support him- or herself. In this investigation a program was devloped to help the small scaled farmer Janne from Sala to keep an energy efficient greenhouse. The program applied machine learning to make predictions of future temperatures in the greenhouse. When the temperature was predicted to be dangerously low for the plants and crops Janne was warned via a HTML web page. To make an as accurate prediction as possible different machine learning algorithm methods were evaluated. XGBoost was the most efficient and accurate method with an cross validation value at 2.33 and was used to make the predictions. The data to train the method with was old data inside and outside the greenhouse provided from the consultancy Bitroot and SMHI. To make predictions in real time weather forecast was collectd from SMHI via their API. The program can be useful for a farmer and can be further developed in the future.

Place, publisher, year, edition, pages
2019. , p. 28
Series
TVE-F ; 19013
Keywords [en]
machinelearning, predictions
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-391045OAI: oai:DiVA.org:uu-391045DiVA, id: diva2:1343546
External cooperation
Bitroot
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2019-08-21 Created: 2019-08-17 Last updated: 2019-08-21Bibliographically approved

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CiteExportLink to record
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  • apa
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