标题： 使用更准确的负荷预测增强能源系统模型 显示英文标题

标题： Enhancing Energy System Models Using Better Load Forecasts

摘要： 能源系统模型需要大量的技术与经济数据，其质量显著影响结果的可靠性。 一些重要数据源ENTSO-E透明度平台上的变量，如输电系统运营商的日前负荷预测，已知存在偏差。 这些偏差和高误差会影响能源系统模型的质量。 我们提出了一种简单的时间序列模型，除了负荷预测历史外不需要任何其他输入变量，以在实时情况下显著改进ENTSO-E透明度平台上输电系统运营商的负荷预测数据，即依次改进每个传入的数据点。 我们进一步介绍了一个专门为短期日前市场开发的能源系统模型。 我们表明，作为输入的改进后的负荷数据可以减少模型的定价误差，特别是在价格高且市场紧张的时候，减少效果尤为明显。 显示英文摘要

摘要： 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 transparency platform, such as transmission system operators' day-ahead load forecasts, are known to be biased. These biases and high errors affect the quality of energy system models. We propose a simple time series model that does not require any input variables other than the load forecast history to significantly improve the transmission system operators' load forecast data on the ENTSO-E transparency platform in real-time, i.e., we successively improve each incoming data point. We further present an energy system model developed specifically for the short-term day-ahead market. We show that the improved load data as inputs reduce pricing errors of the model, with strong reductions particularly in times when prices are high and the market is tight.