Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting

We summarize the methodology of the team Tololo, which ranked first in the load forecasting and price forecasting tracks of the Global Energy Forecasting Competition 2014. During the competition, we used and tested many different statistical and machine learning methods, such as random forests, gradient boosting machines and generalized additive models. In this paper, we only present the methods that showed the best results. For electric load forecasting, our strategy consists of producing temperature scenarios that we then plug into a probabilistic forecasting load model. Both steps are performed by fitting a quantile generalized additive model (quantGAM). Concerning the electricity price forecasting, we investigate three methods that we used during the competition. The first method follows the spirit of that used for the electric load. The second one is based on combining a set of individual predictors. The last one fits a sparse linear regression to a large set of covariates. We chose to present these three methods in this paper because they perform well and show the potential for improvements in future research.