Data Driven Medium Term Electricity Price Forecasting in Ontario Electricity Market and Nord Pool Thesis for the Degree of Master of Science

III " It began with hope and belief " To all people I love IV Acknowledgments I am heartily thankful to my supervisors, Dr. Hamidreza Zareipour and Dr. Tuan. Le, whose encouragement, guidance, and support throughout enabled me to develop an understanding of this subject. I have learned precious lessons from their personality, vision and professionalism. Finally, I'd like to dedicate this work to my beloved family, my lovely sister, Solmaz, my mother, Simin, my father Siamak and my grandfather Shams, and my friends for their encouragement, support, and love which always warms my heart in the darkest moment. Having accurate predictions on market price variations in the future is of great importance to participants in today's electricity market. Many studies have been done on Short Term Price Forecasting (STPF). However, few works can be found in the literature with their main focus on predictions of electricity price in medium term horizon. Generally speaking, Medium Term Price Forecasting (MTPF) has applications where there exist markets for electricity with medium term contracts (e.g., forward/future contracts); Risk management and derivative market pricing, balance sheet calculations, and inflow of " finance solutions " are a few examples of these applications. The goal of this project is to predict the next 12 months monthly average electricity prices in the electricity market of Ontario and Nord Pool. To do so, mathematical models that are known to be capable of predicting series with acceptable accuracy using the limited number of samples available, such as Linear Regression Model (LR), Radial Basis Function Neural Network (RBF-NN), Support Vector Machine (SVM), and Weighted Nearest Neighbor (WNN) are employed. First, different attributes of each market have been studied and the most informative ones, those that can better address future behavior patterns of the price, have been identified. Then, different input parameters designs for each model within each market have been examined. For example, the effect of previous month's price, month indicator, Ontario demand, temperature and gas price is studied. For each market, different models' forecasting results are compared and the most accurate ones are ranked for each market. Following this approach, 12 months ahead electricity prices in both markets have been forecasted. The Mean Absolute Percentage Error (MAPE) for each model in each market is calculated by dividing the difference between forecasted and actual price of a month, by its actual price. In the case of Nord Pool different models …