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Energy system models require a large amount of technical and economic data,the quality of which significantly influences the reliability of the results.Some of the variables on the important data source ENTSO-E transparencyplatform, such as transmission system operators' day-ahead load forecasts, areknown to be biased. These biases and high errors affect the quality of energysystem models. We propose a simple time series model that does not require anyinput variables other than the load forecast history to significantly improvethe transmission system operators' load forecast data on the ENTSO-Etransparency platform in real-time, i.e., we successively improve each incomingdata point. We further present an energy system model developed specificallyfor the short-term day-ahead market. We show that the improved load data asinputs reduce pricing errors of the model, with strong reductions particularlyin times when prices are high and the market is tight.

Enhancing Energy System Models Using Better Load Forecasts