Enhanced Machine Learning Techniques for System Imbalance Forecasting

The unpredictable nature of renewable energy sources (RES) has become a major cause of imbalance between electricity generation and demand, which the Transmission System Operator (TSO) must handle in real-time operation. The RES stochasticity and the often-excessive imbalance quantities have increased the financial risks for RES aggregators. Consequently, forecasting system imbalance has become crucial for RES aggregators who seek to avoid these charges. This paper implements contemporary real-time machine learning models to forecast system imbalance and evaluates them based on forecasting accuracy. Qualitative predictions stem from a classification problem determining the system’s direction: whether there is energy overgeneration (“long” system) or production deficit (“short” system). This prediction, combined with a regression problem’s solution and confidence interval calculations, offers deeper insight into the system imbalance quantity. A case study for the Greek power system is presented along with the respective test results, demonstrating the effectiveness of the proposed methods for predicting both system imbalance direction and magnitude. The forecasts are further compared against naïve benchmarks and through statistical significance tests, while the probabilistic forecasts are embedded in a risk-constrained (CVaR-based) market positioning problem, which, in an ex post application for a Greek RES aggregator, yields savings of €0.98–1.34 million over a four-month period.