Year: 2022
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G1072749480, G1523888516, G2510020596, G260750634, Nonlocal Methods for Arbitrary Data Sources, G305640112, G3345895242, G3477743816, G4149313689, G5084427092, A Center for Brains, Minds and Machines: the Science and the Technology of Intelligence, G7809643752, G8289759875, G8512683158, G8656780544, G9000189046, G9644609
OpenAlex ID: W4226513809