dreamhui's starred repositories
Decentralized-Scheduling-Strategy-of-Heating-Systems
RWTH Bachelor’s thesis: Optimization algorithm that balances the residual load in microgrids with heat pumps and combined heat / power units, while maintaining data privacy and economical fairness.
predict_Lottery_ticket_pytorch
pytorch下基于transformer / LSTM模型的彩票预测
CEEMDAN-and-LSTM-CNN-CBAM
This repo holds the implementation the paper 'Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM', by Yanhui Liang, Yu Lin, and Qin Lu.
Power_Load_Forecasting_by_TCN
本科毕业设计：基于TCN的电力负荷预测算法
DeepLearningForTSF
深度学习以进行时间序列预测
awesome-machine-learning-resources
A curated list of awesome lists across all machine learning topics. | 机器学习/深度学习/人工智能一切主题 (学习范式/任务/应用/模型/道德/交叉学科/数据集/框架/教程) 的资源列表汇总。
tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
tensorflow-federated
An open-source framework for machine learning and other computations on decentralized data.
pytorch_federated_learning
PyTorch Federated Learning (easy to use and extend)
switch-china-open-model
Open model and data for SWITCH-China
Energy-management-MIP-Deep-Reinforcement-Learning
source code for the paper:A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatch
burleyson-etal_2021_applied_energy
Meta repository for data and code associated with the Burleyson et al. 2021 paper in Applied Energy.
AppliedEnergy2021
This repository contains the test data and code used for the paper titled "Physics-Based Machine Learning Framework for Predicting NOx Emissions from Compression-Ignition Engine Powered Vehicles" that is submitted to the Applied Energy Journal
burleyson-etal_2024_applied_energy
Meta repository for data and code associated with the Burleyson et al. 2024 submission to Applied Energy.
Mr.-Ranedeer-AI-Tutor
A GPT-4 AI Tutor Prompt for customizable personalized learning experiences.
Grid_Scale_Energy_Storage_Q_Learning
Final Project for AA 228: Decision-Making under Uncertainty Abstract: Grid-scale energy storage systems (ESSs) are capable of participating in multiple grid applications, with the potential for multiple value streams for a single system, termed "value-stacking". This paper introduces a framework for decision making, using reinforcement learning to analyze the financial advantage of value-stacking grid-scale energy storage, as applied to a single residential home with energy storage. A policy is developed via Q-learning to dispatch the energy storage between two grid applications: time-of-use (TOU) bill reduction and energy arbitrage on locational marginal price (LMP). The performance of the dispatch resulting from this learned policy is then compared to several other dispatch cases: a baseline of no dispatch, a naively-determined dispatch, and the optimal dispatches for TOU and LMP separately. The policy obtained via Q-learning successfully led to the lowest cost, demonstrating the financial advantage of value-stacking.
aco-thermal-dispatch
Economic Dispatch of Thermal Units via Ant Colony Optimization - Otimização por Colônia de Formigas aplicada ao Despacho Econômico de Unidades Térmicas
auto-draft
基于GPT4的文献总结工具.
ChatGLM-6B
ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
prompt-patterns
Prompt 编写模式：如何将思维框架赋予机器，以设计模式的形式来思考 prompt