Forecasting Nord Pool day-ahead prices with an autoregressive model
Forecasting Nord Pool day-ahead prices with an autoregressive model
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使用自回归模型预测 Nord Pool 日前价格
DOI：
10.1016/j.enpol.2012.06.028
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发表时间：
2012
期刊：
影响因子：
9
通讯作者：
T. Kristiansen
中科院分区：
文献类型：
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作者：
T. Kristiansen
本文提出了一个模型来预测诺德池小时日前价格。该模型基于Weron和Misiorek（2008年），但在估计参数方面有所减少（从24套减少到1套），并进行了修改，以将北欧需求和丹麦风力发电作为外生变量。我们在分析期间的所有时间内建模价格，而不是在24小时的每个小时内建模。通过对诺德池数据应用三种模型变体，我们实现了约6-7%的每周平均绝对百分比误差（WMAE）和8%至11%的每小时平均绝对百分比误差（MAPE）。样本结果产生约5%的WMAE和每小时MAPE。这些模型使分析师和交易员能够准确地预测每小时的前一天价格。此外，这些模型相对简单，易于实施。它们可以在任何贸易组织中建立。
This paper presents a model to forecast Nord Pool hourly day-ahead prices. The model is based on Weron and Misiorek (2008) but reduced in terms of estimation parameters (from 24 sets to 1) and modified to include Nordic demand and Danish wind power as exogenous variables. We model prices across all hours in the analysis period rather than across each single hour of 24hours. By applying three model variants on Nord Pool data, we achieve a weekly mean absolute percentage error (WMAE) of around 6–7% and an hourly mean absolute percentage error (MAPE) ranging from 8% to 11%. Out of sample results yields a WMAE and an hourly MAPE of around 5%. The models enable analysts and traders to forecast hourly day-ahead prices accurately. Moreover, the models are relatively straightforward and user-friendly to implement. They can be set up in any trading organization.