@misc{MoebiusWatermeyerGrotheetal., author = {M{\"o}bius, Thomas and Watermeyer, Mira and Grothe, Oliver and M{\"u}sgens, Felix}, title = {Enhancing energy system models using better load forecasts}, series = {Energy Systems}, journal = {Energy Systems}, issn = {1868-3975}, doi = {10.1007/s12667-023-00590-3}, pages = {1 -- 30}, abstract = {Since energy system models require a large amount of technical and economic data, their quality significantly affects the reliability of the results. However, some publicly available data sets, such as the transmission system operators' day-ahead load forecasts, are known to be biased and inaccurate, leading to lower energy system model performance. We propose a time series model that enhances the accuracy of transmission system operators' load forecast data in real-time, using only the load forecast error's history as input. We further present an energy system model developed specifically for price forecasts of the short-term day-ahead market. We demonstrate the effectiveness of the improved load data as input by applying it to this model, which shows a strong reduction in pricing errors, particularly during periods of high prices and tight markets. Our results highlight the potential of our method the enhance the accuracy of energy system models using improved input data.}, language = {en} } @misc{WatermeyerMoebiusGrotheetal., author = {Watermeyer, Mira and M{\"o}bius, Thomas and Grothe, Oliver and M{\"u}sgens, Felix}, title = {A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling}, series = {arXiv}, journal = {arXiv}, doi = {10.48550/arXiv.2304.09336}, pages = {1 -- 38}, abstract = {The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently.}, language = {en} }