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Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight), https://openreview.net/forum?id=JePfAI8fah
Wind power prediction using LSTM
Code for "Is Mamba Effective for Time Series Forecasting?"
Official implementation of the paper "Frequency-domain MLPs are More Effective Learners in Time Series Forecasting"
Code for paper named 'Physics-Inform Wind Estimation for Predictive Yaw Control of Utility-Scale Wind Turbines'
HYPER: A PINN approach to reconstruct 3D wind fields from meteor measurements
PyTorch Implementation of Physics-informed Neural Networks
Must-read Papers on Physics-Informed Neural Networks.
PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.
An accurate and reliable wind power forecasting model that can handle the variability and uncertainty of the wind resource. An ensemble model which includes the Transformer, LSTM and Gradient Boost…
Code for paper "Sparse Variational Gaussian Process based Day-ahead Probabilistic Wind Power Forecasting", IEEE Transactions on Sustainable Energy
Wind power forecasting demo using XGBoost with physical insights.
This project involves the development and deployment of a wind power forecasting application leveraging machine learning and deep learning techniques. The application predicts wind power using key …
Predicting future wind power from wind speed and direction forecasts.
This work proposes a wind power prediction model, in which the proposed model uses self-attention to capture the long-range relationship and uses convolutional layers to learn the local temporal i…
In this project, we demonstrate a method based on Reinforcement Learning for the hybrid optimization of a future solar PV and wind power integrated energy system.
This project develops an LSTM neural network model for short-term wind speed forecasting. Accurate predictions are essential for maintaining power grid stability and efficiency, especially with ren…
A Deep Learning model that predict forecast the power generated by wind turbine in a Wind Energy Power Plant using LSTM (Long Short Term Memory) i.e modified recurrent neural network.
A CNN-BiLSTM short-term wind power forecasting model incorporating adaptive boosting
Multivariate Time Series LSTM and Random Forest Models for Wind Power Forecasting.
wind power forecasting with ensemble techniques
Apply machine learning techniques in Python to forecast wind power production.
Renewable Energy Seminar (3ECTS): Predict power generated by wind farms for different horizons