hilbert-qyw Goto Github PK

tensorfow-rbm

Tensorflow implementation of Restricted Boltzmann Machine

test1

project test

the-state-of-charge-predication-of-lithium-ion-battery-using-data-driven-machine-learning

time-series-transformers-review

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

time-series-works-conferences

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, etc.)

time_series_augmentation_recommendation

An example of time series augmentation methods with Keras

transfer-learning-based-state-of-health-estimation-for-lithium-ion-battery

transfer-learning-library

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

transferlearning

Everything about Transfer Learning and Domain Adaptation--迁移学习

ts_augmentation

A Python package for time series augmentation

tutorials

PyTorch tutorials.

two_stage_rul_prediction

The code is for the paper "Ma et al. A Two-Stage Integrated Method for Early Prediction of Remaining Useful Life of Lithium-ion Batteries"

udtl

Source codes for the paper "Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study" published in TIM

umnn

Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.

vcnn

vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

wake_steering_of_wind_turbines_using_nrel-s_floris

Increased 3x3 wind farm output by 18.5\% using NREL's FLORIS library in Python with optimum yaw angles to each wind turbine Studied 4 wake models, fatigue life of turbine, optimal power outputs for 3x3 wind farm caused by yaw misalignment of wind turbines

wind-speed-forecasting-for-wind-power-generation-plant.-neural-network-ml-based-prediction-algo.-

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 (ANN) approach to get a prediction of wind speed using historical wind speed data. The historical data used here were gathered from NREL website ,as hourly basis from 80 meter hub height. The measurement location is NREL Flatirons Campus (M2). The readings displayed are derived from instruments mounted on or near a 82 meter (270 foot) meteorological tower located at the western edge of the Flatirons Campus (formerly NWTC) and about 11 km (7 miles) west of Broomfield, and approximately 8 km (5 miles) south of Boulder, Colorado. The tower is located at 39o 54' 38.34" N and 105o 14' 5.28" W (datum WGS84) with its base at an elevation of 1855 meters (6085 feet) above mean sea level. Data from year 2014 to 2018, in total 5 years of data has been used here as dataframe. Here the neural network has been implemented by Tensorflow’s Keras API. The used model is “sequential”. Four dense layer has been used in the optimized model. LSTM(Long- short-term memory) architecture has been used here as neural network architecture. Activation function being used in the dense layers are dropout function. The optimizer being used here is Adam. Here various range of Dropout function has been examined and chosen the best fit for this model. Also this paper examined various kinds of optimization method and used the best fitted one. The model performances were evaluated using the mean squared error using adam optimizer. Various kinds of data analytic techniques has been used here for better visualization and in depth understanding of the dataset and its variables. Since it is mostly a time series data so in the analytic part how the data is being changed with time has been shown. From the result of the predicted dataset it can be state that, this wind speed prediction model works best for all kinds of winds speed besides overfitted/ abnormal wind speeds which is a very rare case scenario.

windfinder

Analysis of NREL Wind Toolkit dataset using AWS clusters