somniferume's repositories
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
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
Deep-Reinforcement-Learning-for-MicroGrids
A Deep Reinforcement Learning based approach for energy supply management in MicroGrids
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.
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-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
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
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!
MDP-DP-RL
Markov Decision Processes, Dynamic Programming and Reinforcement Learning
ML-assignments
about Regression, Classification, CNN, RNN, Explainable AI, Adversarial Attack, Network Compression, Seq2Seq, GAN, Transfer Learning, Meta Learning, Life-long Learning, Reforcement Learning.
NiceLab
We are studying Applied Machine Learning and Optimization for Energy, Power System
PCA-
源自百度推送的哈工大硕士生实现的11种降维算法
pymdptoolbox
Markov Decision Process (MDP) Toolbox for Python
Python-100-Days
Python - 100天从新手到大师
Python-time-series-rolling-forecasting
You can use this code to learn increasing window rolling forecast
rl-book
Source codes for the book "Reinforcement Learning: Theory and Python Implementation"
RobustSTL
Unofficial Implementation of RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series (AAAI 2019)
simple_rl
A simple framework for experimenting with Reinforcement Learning in Python.
sktime-dl
sktime companion package for deep learning based on TensorFlow
SolarEnergyPrediction
Use historical energy production values along with weather predictions to forecast photovoltaic energy production.
solarhome-control-bench
open testbench for control and optimization methods for the energy management of a simple solar home
tsfresh
Automatic extraction of relevant features from time series: