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citylearnes

Official reinforcement learning environment for demand response and load shaping

coop_games

cooperative game theory to allocate a fixed amount of power

cooperative-competitivel

cooperative-competitive learning algorithm for two neurons

cooperative-games

The objective of this code is to implement a cooperative optimization algorithm that allocates a fixed amount of power from an electric system to a group of agents in a way that maximizes their collective satisfaction.

dqn-dr

Q-learning algorithm to learn and update optimal pricing strategies based on the state of the market.

energy_tools

Data processing and analysis tasks related to energy consumption and temperature.

lstm-dqn

The Python script implements two models for predicting the next values in a sequence, a simple LSTM neural network trained using Keras, and a Deep Q-Network (DQN) that uses a Q-learning algorithm.

lstm-neural-network

This code is an implementation of an LSTM neural network for making time series predictions.

pricing-optimal

Demand-Response (Sequential Least Squares Programming)

rl

sales_qlearning

Simple q-learning algorithm based on sales and purchases of an energy company.

set_incentive

Calculate the incentive for the customer.

tangentratesmethod

This code is a tangents method that is used to determine the tariff prices of electricity