|dc.description.abstract||The prices in the Nordic power market are characterized by high volatility. This creates a
demand for securing future power prices. Large hydropower producers use a variety of
instruments to predict price changes, and sign derivatives contracts to secure prices for
parts of their production. In this thesis, we examined how the introduction of machine
learning, in the form of power price predictions, can contribute to risk management for
hydropower producers. More specifically, we focused on the following research question:
How can predictions of the Nordic system price using machine learning methods enhance
decision support for hydropower producers when trading medium-term power derivatives?
To answer this question, we predicted the yearly, quarterly and monthly Nordic system
price for 2018. Predictions of each price was made using the programming language R,
with historical data from 2013 to 2018 retrieved through open sources and Datastream. We
applied eight different machine learning methods, namely linear regression with backwards
selection, ridge regression, lasso regression, partial least squares, regression trees, random
forests, boosting and support vector regression. In addition, we generated forecasts using
ARIMA and NNAR models. To replicate how the decision-making processes of traders
would be in real life, the predicted prices by the three best-performing models on data
prior to 2018 were compared to contract prices at Nasdaq Commodities. Based on the
comparison we determined which futures contracts should be purchased.
The answer to the research question is that machine learning models have great potential
to enhance the decision support for hydropower producers when trading power derivatives.
Compared to a strategy of securing all prices through futures contracts, using the
predictions of the estimated models to decide whether to purchase the contracts led
to the same or a higher gain. To mitigate the risk associated with the models and the
market in general, the predictions made by the models should be used in combination
with existing information and forecasts. The risks associated with the models should also
be incorporated into the general risk management strategy.
Keywords – Machine learning, risk management, futures contracts, hydropower producers||en_US