A new approach to very short term wind speed prediction using k-nearest neighbor classification

By M. Yesilbudak, Ş. Sağiroğlu, Ilhami Colak. 2013

122 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/84d71f6d821c0d6a54b26e624f285cc7e5119eb4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Fuad Un-Noor
2. Wolfgang Schmid
3. Daniel Ambach
4. Eklas Hossain

Citations in the corpus

• A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting (2017, background): “Yesilbudak et al. (2013) argue that physical prediction models use temperature, pressure, orography, roughness and obstacles to perform wind speed forecasts.”
• Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, methodology): “Previously, several prediction models such as fuzzy modeling [145], auto regressive moving average [146], artificial neural network [147], [148], K-nearest neighbor classification [149], computational fluid dynamics (CFD) pre-calculated flow fields [150], extreme learning machine (ELM) [151]–[153], ”