Machine learning in energy forecasts with an application to high frequency electricity consumption data - Erik Heilmann; Janosch Henze; Heike Wetzel (Philipps-University Marburg, School of Business and Economics, 2021)
Forecasting plays an essential role in energy economics. With new challenges and use cases in the energy system, forecasts have to meet more complex requirements, such as increasing temporal and spatial resolution of data. The concept of machine learning can meet these requirements by providing different model approaches and a standardized process of model selection. This paper provides a concise and comprehensible introduction to the topic by discussing the concept of machine learning in the context of energy economics and presenting an exemplary application to electricity load data. For this, we introduce and demonstrate the structured machine learning process containing the preparation, model selection and test of forecast models. This process is intended to serve as a general guideline for energy economists and practitioners who need to apply sophisticated forecast models.
Keywords: C45; C53; Q47; 330; machine learning; electricity consumption forecast; arti cial neural network; time series forecast