AIMC Topic: Wind

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Modeling Soil Temperature for Different Days Using Novel Quadruplet Loss-Guided LSTM.

Computational intelligence and neuroscience
Soil temperature ( ), a key variable in geosciences study, has generated growing interest among researchers. There are many factors affecting the spatiotemporal variation of , which poses immense challenges for the estimation. To enrich processi...

Using multiple linear regression and BP neural network to predict critical meteorological conditions of expressway bridge pavement icing.

PloS one
Icy bridge deck in winter has tremendous consequences for expressway traffic safety, which is closely related to the bridge pavement temperature. In this paper, the critical meteorological conditions of icy bridge deck were predicted by multiple line...

Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Environmental monitoring and assessment
Accurate prediction of the reference evapotranspiration (ET) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinatio...

Artificial neural network-based adaptive control for a DFIG-based WECS.

ISA transactions
This paper presents an artificial neural network-based adaptive control approach for a doubly-fed induction generator (DFIG) based wind energy conversion system (WECS). The control objectives are: (1) extraction of maximum available power from the wi...

Weather forecasting based on data-driven and physics-informed reservoir computing models.

Environmental science and pollution research international
In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditio...

Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system.

Environmental science and pollution research international
The solar photovoltaic system is an emerging renewable energy resource. The performance of the solar photovoltaic system is predicted based on the historical experimental dataset. In this work, the real-time prediction models are developed for the ou...

Oscillations make a self-scaled model for honeybees' visual odometer reliable regardless of flight trajectory.

Journal of the Royal Society, Interface
Honeybees foraging and recruiting nest-mates by performing the waggle dance need to be able to gauge the flight distance to the food source regardless of the wind and terrain conditions. Previous authors have hypothesized that the foragers' visual od...

Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA.

Environmental science and pollution research international
People's sentiments and perceptions of greenhouse gas emission and renewable energy are important information to understand their reaction to the planned mitigation policy. Therefore, this research analyzes people's perceptions of greenhouse gas emis...

Short-term wind speed prediction using hybrid machine learning techniques.

Environmental science and pollution research international
Wind energy is one of the potential renewable energy sources being exploited around the globe today. Accurate prediction of wind speed is mandatory for precise estimation of wind power at a site. In this study, hybrid machine learning models have bee...

Optimization scheme of wind energy prediction based on artificial intelligence.

Environmental science and pollution research international
Wind energy, as one of the renewable energies with the most potential for development, has been widely concerned by many countries. However, due to the great volatility and uncertainty of natural wind, wind power also fluctuates, seriously affecting ...