Forecasting cross-border power transmission capacities in Central Western Europe using artificial neural networks

摘要: Flow-based Market Coupling (FBMC) provides welfare gains from cross-border electricity trading by efficiently providing coupling capacity between bidding zones. In the coupled markets of Central Western Europe, common regulations define FBMC methods, but transmission system operators keep some degrees freedom in parts calculation. Besides, many influencing factors flow-based domain, making it difficult to fundamentally model calculation and derive reliable forecasts it. light this challenge, given contribution reports findings attempt domain applying Artificial Neural Networks (ANN). As target values, Maximum Bilateral Exchanges (MAXBEX) have been chosen. Only publicly available data has used as inputs make approach reproducible for any market participant. It is observed that forecast derived ANN yields similar results a simple carry-forward method one-hour forecast, whereas longer-term up twelve hours ahead, network outperforms trivial approach. Nevertheless, overall low accuracy prediction strongly suggests more detailed understanding structure evolution its relation underlying infrastructure characteristics needed allow participants robust FMBC parameters.