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Short-term wind power forecasting using artificial neural networks
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
Abstract [en]

Wind power has seen a tremendous growth in recent years and isexpected to grow even more in years to come. In order to better scheduleand utilize this energy source good forecasting techniques are necessary. This thesis investigates the use of artificial neural networks for short-term wind power prediction. It compares two different networks, the so called Multilayer Perceptron and the Hierarchical Temporal Memory / CorticalLearning Algorithm. These two networks are validated and compared on a benchmark dataset published in the Global Energy Forecasting Competition, a competition used for short-term wind power prediction. The results of this study show that the Multilayer Perceptron is able to compete with previously published models and that Hierarchical Temporal Memory/ Cortical Learning Algorithm is able to beat the reference model.

Place, publisher, year, edition, pages
National Category
Computer Science
URN: urn:nbn:se:kth:diva-176018OAI: diva2:865336
Subject / course
Computer Science
Educational program
Master of Science - Machine Learning
Available from: 2015-10-27 Created: 2015-10-27 Last updated: 2015-10-27Bibliographically approved

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