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Wind Speed Prediction using LSTMs in PyTorch (https://arxiv.org/pdf/1707.08110.pdf)

For largescale wind power penetration Wind speed prediction is a basic requirement of wind energy generation. There are many artificial neural network (ANN), ARMA, ARIMA approaches proposed in the recent literature in order to tackle this problem. This paper will use the artificial neural network... Read more

LSTM neural network realizes the prediction of wind speed through the learning of various parameters. It can provide important support for the smooth operation of power system and the optimization of control strategy. The fuzzy rough set theory is used to reduce many factors that affect wind spee... Read more

Spatiotemporal graph convolutional network for wind speed prediction.

Ultra-short-term multi-step wind speed prediction for wind farms based on adaptive noise reduction technology and temporal convolutional network

[Engineering Letters]An Improved Hybrid CNN-LSTM-Attention Model with Kepler Optimization Algorithm for Wind Speed Prediction

Wildfire Risk Prediction is a machine learning project that uses synthetic environmental data to estimate wildfire probability. It analyzes factors like temperature, humidity, wind speed, rainfall, and drought index to classify risk levels.

PyTorch code for the Paper "Wind speed prediction using multidimensional convolutional neuralnetworks"

A project focused on forecasting solar photovoltaic (PV) power generation using regional microclimate data. Implements machine learning models like CatBoost, LightGBM, and XGBoost for predictions, leveraging environmental features like temperature, humidity, wind speed, and solar radiation.

Web Interface for Wind Speed Prediction