Revisión de literatura de modelos computacionales para la predicción de la velocidad del viento de 2004 a 2016
In one sentence. Reviews 2004–2016 literature on wind speed forecasting models, classifying regression, neural network, time series, fuzzy logic and Gaussian process approaches; ANN and fuzzy logic are flagged as future directions.
Abstract
Wind speed forecast is a topic broadly researched in the present day. Its importance in the design and calculations of structures for considerably sized buildings to be built in high risk hurricane zones is a key factor to consider. Another branch off this line of research includes its utility in Eolic energy prediction, which is not a subject to discuss in this article. Here a classification of computational models for wind speed forecasting is presented according to the mathematic technique used for forecasting and based on literature review. The techniques used to classify are: multivariate lineal regression, artificial neural networks, time series, fuzzy logic, and the Gaussian process. Throughout the development of this research, artificial neural networks and fuzzy logic applications were identified as future lines of research both combined with efficient data mining work.
Labels
- Topic
- Local & station forecasting
- Type
- Review, survey or tutorial
- Method
- Classical ML (trees, GP, linear, kernels), Other
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.36825/riti.08.15.004 OpenAlex:W3007159084
- Added
- 2026-10-22
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