Abstract
The electricity market is driven by complicated interactions that are hard to model analytically. This is particularly the case for the balancing market, where imbalances between supply and demand after the day-ahead market clearance are balanced. The balancing market bridges the gap between the day-ahead market and the actual power system operations. Being able to predict the necessary balancing volumes and prices some hours in advance of the operational hour will allow power producers to plan their production and trading in a more optimal way. There exist large amounts of open data that could contain predictive information about the balancing market, including day-ahead market data and climatic data. However, the literature on forecasting volume and prices in the balancing market is sparse compared to the rich literature on forecasting for the day-ahead market. Neural networks are powerful functional approximators and well-suited to model the complex relationships in the power market. It may also be used to study the predictability of the balancing volumes and prices forward in time. In this paper, we develop a model based on long short-term memory (LSTM) recurrent neural networks to predict volumes and prices in the Nordic balancing market based on public accessible data. Results show that the LSTM model performs well when compared to the two baselines selected. However, the performance is not significantly better, which indicates that the market data does not hold significant predictive information.
