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activityrecognition

Resources about activity recognition-行为识别资料

ai-residential-electricity-bill-prediction

Prediction of Residential Electricty Bill Amount using Aritificial Intelligence

an-ann-lstm-based-model-for-learning-individual-customer-behavior-in-response-to-electricity-prices

An ANN-LSTM based Model for Learning Individual Customer Behavior in Response to Electricity Prices

ausgrid-solar-data

Personal analysis of the "Solar home electricity" dataset from Ausgrid

bayesian-optimization

bayesian-optimization

building-data-genome-project-2

Whole building non-residential hourly energy meter data from the Great Energy Predictor III competition

citylearn

Official reinforcement learning environment for demand response and load shaping

cnn-bilstm-attention-time-series-prediction_keras

CNN+BiLSTM+Attention Multivariate Time Series Prediction implemented by Keras

css_mm_4e

da-electricity-price-forecasting

Forecasting Day-Ahead electricity prices in the German bidding zone with deep neural networks.

data-analysis

用 jupyter notebook做的一些机器学习项目

deep-reinforcement-learning-for-microgrids

A Deep Reinforcement Learning based approach for energy supply management in MicroGrids

deep-rl

Collection of Deep Reinforcement Learning algorithms

dqlearning-toolbox

A Toolbox for deep reinforcement learning(QLearning)

drl-for-microgrid-energy-management

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.

electric-power-hourly-load-forecasting-using-recurrent-neural-networks

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-energy

Forecasting Solar Power: Analysis of using a LSTM Neural Network

gym-homeenergy

Reinforcement learning based home energy saving project

hem-deeprl-v2

Home Energy Management based on Deep Reinforcement Learning Approach.

hems

Home Energy Management System for Small Prosumers Considering Electric Vehicle Load Scheduling

hems-1

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.

individual-home-electricity-consumption-analysis

Models for predicting electricity demand and classifying homes based on their patterns of energy use

iot-analytics-smart-home-energy-forecasting

Applying time series to predict Smart Home energy consumption and demonstrating how data can enable households to save energy (IoT Analytics)

irradiance-rnn

Recurrent neural network for forecasting solar irradiance :sunny:

load_forecasting

Load forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models

lstm_vst_pvgf

Multi-scale LSTM based hourly Photovoltaic (PV) power generation forecasting

machine-learning-is-all-you-need

🔥🌟《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!

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

machinelearning_smartmeteranalytics

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.

markov-decision-processes

Implementation of value iteration algorithm for calculating an optimal MDP policy