论文标题

物理知情的机器学习风速预测

Physics Informed Shallow Machine Learning for Wind Speed Prediction

论文作者

Lagomarsino-Oneto, Daniele, Meanti, Giacomo, Pagliana, Nicolò, Verri, Alessandro, Mazzino, Andrea, Rosasco, Lorenzo, Seminara, Agnese

论文摘要

预测风的能力对于能源生产和天气预报都至关重要。构成传统预测基础的机械模型在地面附近的性能差。在本文中,我们根据监督学习采用替代数据驱动的方法。我们分析了从意大利两个中部和西北地区(Abruzzo和Liguria)位于位于32个位置的10 m高度高度的风洞测得的大量风数据集。我们使用过去的风史来训练监督的学习算法,以预测未来时间的价值(地平线)。使用来自单个位置和时间范围的数据,我们可以系统地比较几种算法,其中我们可以更改输入/输出变量,输入的内存以及线性与非线性学习模型。然后,我们比较所有位置和预测视野中最佳算法的性能。我们发现最佳设计以及其性能随位置而异。我们证明,可再现的昼夜周期的存在为理解这种变化提供了理由。我们以系统的比较与最先进的算法进行了系统的比较,并表明,当模型精确设计时,浅算法在更复杂的深度体系结构中具有竞争力。

The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the ground. In this paper, we take an alternative data-driven approach based on supervised learning. We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in two central and north west regions of Italy (Abruzzo and Liguria). We train supervised learning algorithms using the past history of wind to predict its value at a future time (horizon). Using data from a single location and time horizon we compare systematically several algorithms where we vary the input/output variables, the memory of the input and the linear vs non-linear learning model. We then compare performance of the best algorithms across all locations and forecasting horizons. We find that the optimal design as well as its performance vary with the location. We demonstrate that the presence of a reproducible diurnal cycle provides a rationale to understand this variation. We conclude with a systematic comparison with state of the art algorithms and show that, when the model is accurately designed, shallow algorithms are competitive with more complex deep architectures.

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