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Data streams and reservoir sampling for predicting production of solar power plants

Denis Kotnik (2018) Data streams and reservoir sampling for predicting production of solar power plants. MSc thesis.

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    In the electrical power systems the efficient storage of electricity is almost impossible, therefore the electrical distributors are forced to deal with the problem of maintaining a balance between consumption and production of electricity. Quality forecasts of electricity production and consumption make this problem easier. This thesis deals with the short-term forecasting of electricity production from solar power plants for the Primorska region in Slovenia, whereby data is treated as a data stream. Attributes used for this predictions are usually obtained from weather forecast model. Classical machine learning algorithms as well as algorithms that are capable of online/incremental learning are being used for forecasting power production and mutually comparison. Machine learning algorithms are being upgraded with ADWIN algorithm, which detects concept drifts and maintains a sample of the last examples using adaptive size sliding window. A reservoir sam- pling algorithm with exponential decay of older elements is also being used to maintain a sample from the entire data stream. Useful predictions with a performance comparable to other results have been obtained with online algorithms learned on the sample of the data stream.

    Item Type: Thesis (MSc thesis)
    Keywords: data mining, data streams, concept drift, reservoir sampling, solar power plants, production forecasting
    Number of Pages: 109
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    izr. prof. dr. Matjaž Kukar267Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537785795)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 4100
    Date Deposited: 14 Mar 2018 16:30
    Last Modified: 09 May 2018 12:57
    URI: http://eprints.fri.uni-lj.si/id/eprint/4100

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