ainiusheng Goto Github PK

achp

A python-based air conditioning and heat pump system model

agent-based-modeling-in-electricity-market-using-ddpg-algorithm

Agent-Based Modeling in Electricity Market Using Deep Deterministic Policy Gradient Algorithm

bilevel-optimization

Papers projects about Bilevel Optimization and Machine Learning

bilevel-optimization-emissions

Proposed a mathematical model for optimizing the profits and emissions while setting dynamic prices of electricity. A bilevel & multi-objective model is proposed for maximizing profits of retailer, minimizing the emissions produced, & minimizing the total cost of customers.

co2-heat-pump-system-optimization

Co-simulation with dymola, and reinforcement learning is deployed on Python.

energytradingdissertationmodel

This is my attempt at modeling an intraday energy trading scenario for three different locations. I use the Gurobi platform to optimize revenue over the course of June 2019 for a commercial building energy system with a solar plant, on-site energy storage and an electric heat pump flexible load. Implemented for three locations in Spain, Germany and the UK.

gan_scenario_forecasting

Computing Scenario Forecasting Based on Generative Adversarial Networks (GAN)

ieee-5-bus-power-system

The source files for paper: OPF-based Under Frequency Load Shedding Tracking the Dynamic Frequency Trajectory

k-mediods

The aim of this project is to implement k-mediods algorithm of unsupervised learning from scratch. 3 random numpy arrays(2-D) have been taken into consideration for this project. This code can be used to partition any given dataset into 'n' clusters where n can be any real number of user's choice.

machinelearning

Machine Learning in Action学习笔记，一个文件夹代表一个算法，每个文件夹包含算法所需的数据集、源码和图片，图片放在pic文件夹中，数据集放在在Data文件夹内。书中的代码是python2的，有不少错误，这里代码是我用python3写的，且都能直接运行

pandapower

Convenient Power System Modelling and Analysis based on PYPOWER and pandas

parl

A high-performance distributed training framework for Reinforcement Learning

renewables_scenario_gen_gan

The implementation of scenario generation for renewables production process

scenario-forecasts-gan

Research code

seqgan

Implementation of Sequence Generative Adversarial Nets with Policy Gradient

seqgan_tensorflow_stock_predict

Please run python train.py to predict time series.

stock-price-prediction-using-gan

In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

wseqgan-gp_music