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.

Preview

PDF Download (25Mb)

Abstract

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.

Actions (login required)