| dc.description.abstract | In this master thesis we have worked with seven different machine learning methods to discover
which algorithm is best suited for predicting the next-day electricity price for the Norwegian price
area NO1 on Nord Pool Spot. Based on historical price, consumption, weather and reservoir data,
we have created our own data sets. Data from 2001 through 2009 was gathered, where the last one
third of the period was used for testing. We have tested our selected machine learning methods
on seven different subsets. We have used the following machine learning algorithms: model trees,
linear regression, neural nets, RBF networks, Gaussian process, support vector machines and evolutionary
computation. Through our experiments we have found that a support vector machine using
an RBF kernel has the best prediction ability for predicting the NO1 electricity price. We have made
several interesting observations that can serve as a basis for further work in the topic of electricity
price prediction for Nord Pool Spot. | en_US |
