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Machine Learning for Solar Energy Prediction
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences.
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This thesis consists of the study of different Machine Learning models used to predict solar power data in photovoltaic plants.

The process of implement a model of Machine Learning will be reviewed step by step: to collect the data, to pre-process the data in order to make it able to use as input for the model, to divide the data into training data and testing data, to train the Machine Learning algorithm with the training data, to evaluate the algorithm with the testing data, and to make the necessary changes to achieve the best results.

The thesis will start with a brief introduction to solar energy in one part, and an introduction to Machine Learning in another part. The theory of different models and algorithms of supervised learning will be reviewed, such as Decision Trees, Naïve Bayer Classification, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Linear Regression, Logistic Regression, Artificial Neural Network (ANN).

Then, the methods Linear Regression, SVM Regression and Artificial Neural Network will be implemented using MATLAB in order to predict solar energy from historical data of photovoltaic plants. The data used to train and test the models is extracted from the National Renewable Energy Laboratory (NREL), that provides a dataset called “Solar Power Data for Integration Studies” intended for use by Project developers and university researchers. The dataset consist of 1 year of hourly power data for approximately 6000 simulated PV plants throughout the United States.

Finally, once the different models have been implemented, the results show that the technique which provide the best results is Linear Regression.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Machine Learning, Solar Energy, Forecasting, MATLAB, Supervised Learning, Regression Model
National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hig:diva-27423OAI: oai:DiVA.org:hig-27423DiVA, id: diva2:1225422
Educational program
Electronics – bachelor’s programme (in eng)
Supervisors
Examiners
Available from: 2018-06-27 Created: 2018-06-27 Last updated: 2018-06-27Bibliographically approved

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Department of Electronics, Mathematics and Natural Sciences
Engineering and TechnologyElectrical Engineering, Electronic Engineering, Information Engineering

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CiteExportLink to record
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Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • sv-SE
  • en-GB
  • en-US
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  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
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