论文标题
通过机器学习预测电价：预测器敏感性
Forecasting electricity prices with machine learning: Predictor sensitivity
论文作者
论文摘要
目的：在电力市场上进行交易，以使价格结算在交货前（通常是日益）进行。实际上，这些价格高度波动，因为它们在很大程度上取决于一系列变量，例如电力需求和可再生能源的进料。因此，需要准确的预测。 方法：本文旨在比较供应侧（太阳能和风能发电），需求侧，与燃料相关和经济影响的不同预测因素。因此，我们通过机器学习实施了广泛的非线性模型，并借鉴了基于信息融合的灵敏度分析。 调查结果：我们解散了每个预测指标的各自相关性。我们表明，外部预测变量将根平方误差降低多达21.96％。从统计学上讲，迪伯尔德·马里亚诺（Diebold-Mariano）测试证明了所提出的机器学习模型的预测准确性优越。 独创性：添加进一步预测变量的好处直到最近才获得牵引力；但是，关于单个变量如何促进机器学习预测的贡献知之甚少。
Purpose: Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, accurate forecasts are demanded. Approach: This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, we implement a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis. Findings: We disentangle the respective relevance of each predictor. We show that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior. Originality: The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.
