Time series prediction for the Nordic electricity market
Examensarbete för masterexamen
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|Type: ||Examensarbete för masterexamen|
|Title: ||Time series prediction for the Nordic electricity market|
|Authors: ||Asphult, Gabriel|
|Abstract: ||Trading on the Nordic electricity market depends on accurate predictions about future events of many different characteristics. This type of forecasting, with some satisfactory probability of being correct, can be very hard to manage and hence large computational models need to be used. Electricity is a resource of power that is difficult to store which makes it necessary to produce exactly the same amount as will be consumed at all times. This is why certain companies have an obligation to keep all production and consumption under active control by trading the future power on different markets. Each model used to estimate future production of various types needs to be optimized to take specific information into account. Price trends are also very complex to tell in advance which is why accurate models can give an objective view complementing a personal view. Stochastic optimization is a great tool for creating self-learning models that are able to constantly adapt to changes which is needed when trading electricity. The work was carried out by literature studies and implementation that created an artificial neural network and trained it on historical data about both physical power and financial contract prices. To make the results easier to use open source software was used during the thesis. Randomly generated time series used as test objects show that some transformation before modeling increases speed and accuracy of forecasting by multiple factors. The best model for hydro power production forecasting reduced the average error by almost 40% from the reference case. When forecasting prices for financial contracts the best models give correct direction in 62% of the cases studied. Using stochastic optimization to create various models for the Nordic electricity market is helpful and such models may compete with standard regression models that are mostly used today. One big advantage is the possibility to adapt fast when changes occur. The conclusion of this project is that it is possible for a model to learn typical characteristics of the complex behavior of the electricity market. However, such learning requires knowledge about the market in order to guide the process, as well as a great deal of computational power.|
|Keywords: ||Annan teknik;Other Engineering and Technologies|
|Issue Date: ||2013|
|Publisher: ||Chalmers tekniska högskola / Institutionen för tillämpad mekanik|
Chalmers University of Technology / Department of Applied Mechanics
|Series/Report no.: ||Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden : 2013:43|
|Collection:||Examensarbeten för masterexamen // Master Theses|
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