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Resources about activity recognition-行为识别资料
Prediction of Residential Electricty Bill Amount using Aritificial Intelligence
An ANN-LSTM based Model for Learning Individual Customer Behavior in Response to Electricity Prices
Personal analysis of the "Solar home electricity" dataset from Ausgrid
bayesian-optimization
Whole building non-residential hourly energy meter data from the Great Energy Predictor III competition
Official reinforcement learning environment for demand response and load shaping
CNN+BiLSTM+Attention Multivariate Time Series Prediction implemented by Keras
Forecasting Day-Ahead electricity prices in the German bidding zone with deep neural networks.
用 jupyter notebook做的一些机器学习项目
A Deep Reinforcement Learning based approach for energy supply management in MicroGrids
Collection of Deep Reinforcement Learning algorithms
A Toolbox for deep reinforcement learning(QLearning)
We study the performance of various deep reinforcement learning algorithms for the problem of microgrid’s energy management system. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a population of thermostatically controlled loads, a population of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate between the different sources of flexibility by defining the priority resources, the direct demand control signals and the electricity prices. Seven deep reinforcement learning algorithms are implemented and empirically compared in this paper. The numerical results show a significant difference between the different deep reinforcement learning algorithms in their ability to converge to optimal policies. By adding an experience replay and a second semi-deterministic training phase to the well-known Asynchronous advantage actor critic algorithm, we achieved considerably better performance and converged to superior policies in terms of energy efficiency and economic value.
This project aims to predict the hourly electricity load in Toronto based on the loads of previous 23 hours using LSTM recurrent neural network.
Forecasting Solar Power: Analysis of using a LSTM Neural Network
Reinforcement learning based home energy saving project
Home Energy Management based on Deep Reinforcement Learning Approach.
Home Energy Management System for Small Prosumers Considering Electric Vehicle Load Scheduling
HEMS - Home Energy Management System for a residential solar installation. It enables the user to schedule appliances in a targeted way, increasing energy self-consumption based on energy production predictions via weather forecasts.
Models for predicting electricity demand and classifying homes based on their patterns of energy use
Applying time series to predict Smart Home energy consumption and demonstrating how data can enable households to save energy (IoT Analytics)
Recurrent neural network for forecasting solar irradiance :sunny:
Load forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models
Multi-scale LSTM based hourly Photovoltaic (PV) power generation forecasting
🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!
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
Master Thesis for Energy Engineering #Python. This repository contains codes developed in Python which deals with smart meter analytics. Building consumption dataset from Pecan Street Dataport was obtained along with temperature and irradiance data. The dataset was used to build machine learning models using linear regression, random forest deicision tree, Neural networks and Support vector machines. This repository is still immature. More notebooks will being added and updated in the future.
Implementation of value iteration algorithm for calculating an optimal MDP policy
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.