hotpot1996 Goto Github PK

-2021_machine_learning

李宏毅2021春季机器学习课程课件及作业

an-adaptive-step-size-self-organizing-migration-algorithm-for-short-term-load-forecasting-with-lstm-

An adaptive step size self-organizing migration algorithm for short-term load forecasting with LSTM structure optimization

annotated_deep_learning_paper_implementations

🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

cnn-

Implementation of Electric Load Forecasting Based on CNN.

csdn-

由于CSDN博客里面不能直接上代码链接，涉嫌营销推广，因此建一个github仓库用于整理这些代码链接

deep-reinforcement-learning-algorithms-with-pytorch

PyTorch(强化学习) implementations of deep reinforcement learning algorithms and environments

deeplearningflappybird

(Q学习)Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).

ev-

A bunch of crawlers for extracting data from various sites (site name is mentioned for each one)

ev-station-load-forecasting

电动汽车负荷预测

fleetchargingmanagement

This code was written within the dissertation of Ola Pronobis "Charge management concepts with integrated requirements management in case of unpredictable behavior of electric vehicle fleets" at TU Braunschweig. More information can be found in the published dissertation. The code includes a fleet simulation and an integrated optimization algorithm to dynamically charge a fleet of electric vehicles. Ecological and/or economic goals can be set. The algorithm takes into account the CO2 emissions in the electricity mix, the loads at the site, the driving forecasts, the current electricity prices and the renewable energies generated.

grey-model

使用灰色系统理论做负荷预测。Using Grey System Theory to Make Load Forecasting

hotpot1996

Config files for my GitHub profile.

load-forecasting-ieee-tpwrs-2020

Probabilistic Load Forecasting Based on Adaptive Online Learning (APLF)负荷预测

lstm-

Implementation of Electric Load Forecasting Based on LSTM (BiLSTM). Including direct-multi-output forecasting, single-step-scrolling forecasting, multi-model-single-step forecasting, multi-model-scrolling forecasting, and seq2seq forecasting.

mygit_01

pytorch_learning_notes-b-

Pytorch_Learning_Notes

short-term-load-forecasting

B.Sc Final Project - A system that create images from numeric dataset, and by using Convolutional Neural Network with regression layer, forecast the load on a smart grid (an electrical grid which includes a variety of operation).

timegeopack

Eases playing with geographic and time data