Forecasting Renewable Energy at European Markets

Hun Rim (Università della Svizzera italiana); Juraj Kardos (Università della Svizzera italiana); Olaf Schenk (Università della Svizzera italiana)

Abstract

The ambitious energy targets, accelerated by the recent energy crisis, are driving the European Union to increase the share of renewable energy in gross energy consumption to 42.5% by 2030 from the current 23%. However, the intermittent and seasonal nature of renewable energy sources presents challenges in predicting their production capacity. The ability to accurately forecast the evolution of renewable energy’s stake in the dynamic and ever-evolving energy market is a critical component in the decision making process of policy makers, and market participants alike.
This project aims to explore and evaluate the performance of well-established forecasting methods in anticipating the trends of individual renewable energy components, ultimately contributing to the fostering of a balanced, sustainable, and reliable energy market in the EU. The primary focus is to assess auto-regressive forecasting methods and advanced models incorporating moving-average, exploit seasonality of time series data, or those utilising the correlation with exogenous variables. The results are presented for data considering recent history of the most significant energy component at the European energy markets.