PREDICTING TRADED VOLUMES OF RENEWABLE ENERGY CERTIFICATES : A comparison of different time series forecasting methods

Sammanfattning: Predicting sales is an important step for many business processes. Several forecasting methods have been applied to uncountable different problems, however with no present research found in the area of renewable energy certificates. Thus, this study aims to examine the possibility of developing a model based on traded volumes of certificates, where a comparison between simpler and more complex models explores the general increased interest in machine learning models. Therefore, five different models are tested with monthly sales data: the statistical model Seasonal Autoregressive Integrated Moving Average, the machine learning models Support Vector Regression and Extreme Gradient Boosting and further the neural networks Long Short-Term Memory and Bidirectional Long Short-Term Memory. Extensive data preparation is operated by taking into account seasonality and trends where data transformations are applied in addition to feature engineering. To evaluate the models, non-aggregated monthly forecasts as well as aggregated predictions of two and three months are examined. The results show that it is feasible to model the sales volumes of renewable energy certificates. As expected, the models generally perform better when evaluated based on aggregated monthly predictions. Also, when considering both evaluation strategies, the Seasonal Autoregressive Integrated Moving Average, Support Vector Regression and Extreme Gradient Boosting are the only models showing better performance compared to a baseline model. The proposed solution to enable smarter and more efficient trading decisions today is a combination of the aggregated two months and quarterly predictions of the Seasonal Autoregressive Integrated Moving Average and Support Vector Regression models. Considering an expected expansion of relevant available data for the company, the recommendation for the future is to specifically further develop the machine learning models with an anticipation of improved performance and valuable feature importance insights.