This paper conducts a systemic comparative study on univariate and multivariate wind power forecasting for five wind farms inside the Arctic area. The development of wind power in the Arctic can help reduce greenhouse gas emissions in this environmentally fragile region. In practice, wind power forecasting is essential to maintain the grid balance and optimize electricity generation. This study first applies various learning methods for wind power forecasting. It comprehensively compares the performance of models categorized by whether considering weather factors in the Arctic. Nine different representative types of machine-learning algorithms make several univariate time series forecasting, and their performance is evaluated. It is demonstrated that machine-learning approaches have an insignificant advantage over the persistence method in the univariate situation. With numerical weather prediction wind data and wind power data as inputs, the multivariate forecasting models are established and made one to six h in advance predictions. The multivariate models, especially with the advanced learning algorithms, show their edge over the univariate model based on the same algorithm. Although weather data are mesoscale, they can contribute to improving the wind power forecasting accuracy. Moreover, these results are generally valid for the five wind farms, proving the models' effectiveness and universality in this regional wind power utilization. Additionally, there is no clear evidence that predictive model performance is related to wind farms' topographic complexity.

中文翻译：

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挪威北极地区数据驱动的短期风电预测方法的比较研究

本文对北极地区内五个风电场的单变量和多变量风能预测进行了系统的比较研究。北极风力发电的发展可以帮助减少这个环境脆弱地区的温室气体排放。实际上，风电预测对于维持电网平衡和优化发电至关重要。本研究首先将各种学习方法应用于风电预测。它全面比较了是否考虑了北极的天气因素对模型进行分类的性能。九种不同类型的机器学习算法代表了几种单变量时间序列预测，并对其性能进行了评估。结果表明，在单变量情况下，机器学习方法比持久性方法具有微不足道的优势。以数值天气预报风数据和风能数据为输入，建立了多变量预报模型，并提前了1到6 h进行了预报。多元模型（尤其是具有高级学习算法的模型）在基于相同算法的单变量模型上显示出优势。尽管气象数据是中尺度的，但它们可以有助于提高风电预测的准确性。此外，这些结果通常对五个风电场均有效，证明了该模型在该地区风电利用中的有效性和普遍性。此外，没有明确的证据表明预测模型的性能与风电场的性能有关。