@article {5317,
	title = {Physics informed machine learning for wind speed prediction},
	journal = {Energy},
	volume = {268},
	year = {2023},
	month = {04/2023},
	pages = {126628},
	abstract = {<p>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. Here we take an alternative data-driven approach based on supervised learning. We analyze massive datasets of wind measured from anemometers located at 10\&nbsp;m height in 32 locations in central and north-west Italy. We train supervised learning algorithms using the past history of wind to predict its value at future horizons. Using data from single locations and horizons, we compare systematically several algorithms where we vary the input/output variables, the memory and the linear vs non-linear 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 change with the location. We demonstrate that the presence of a diurnal cycle provides a rationale to understand this variation. We conclude with a systematic comparison with state of the art algorithms. When focusing on publicly available datasets, our algorithm improves performance of 0.3\&nbsp;m/s on average. In the aggregate, these comparisons show that, when the model is accurately designed, shallow algorithms are competitive with deep architectures.</p>
},
	issn = {03605442},
	doi = {10.1016/j.energy.2023.126628},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0360544223000221},
	author = {Lagomarsino-Oneto, Daniele and Meanti, Giacomo and Pagliana, Nicol{\`o} and Verri, Alessandro and Mazzino, Andrea and Rosasco, Lorenzo and Seminara, Agnese}
}
