比较高可再生能源渗透率下电力价格线性模型的预测性能
Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration
AI总结：
本文比较了单变量与多变量模型、频率主义与贝叶斯自回归及向量自回归规范在小时提前电力价格中的表现，涵盖有和无可再生能源发电的四个主要欧洲市场，研究不同可再生能源发电水平下的预测准确性。结果显示，带有外生变量的贝叶斯VAR规范在点预测和密度预测方面均优于其他多变量和单变量规范。
摘要：
本文比较了单变量与多变量模型、频率主义与贝叶斯自回归及向量自回归规范在小时提前电力价格中的表现，涵盖有和无可再生能源发电的四个主要欧洲市场，研究不同可再生能源发电水平下的预测准确性。我们的结果表明，带有外生变量的贝叶斯VAR规范在点预测和密度预测方面均优于其他多变量和单变量规范。
英文摘要：
This paper compares alternative univariate versus multivariate models, frequentist versus Bayesian autoregressive and vector autoregressive specifications, for hourly day-ahead electricity prices, both with and without renewable energy sources. The accuracy of point and density forecasts are inspected in four main European markets (Germany, Denmark, Italy and Spain) characterized by different levels of renewable energy power generation. Our results show that the Bayesian VAR specifications with exogenous variables dominate other multivariate and univariate specifications, in terms of both point and density forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
