A Pytorch DQN and DDPG implementation for a smart home energy management system under varying electricity price.

Transforms electricity spot price into thermostat control signal. Home Assistant custom component.

OpenADR protocol java implementation: https://www.openadr.org/

Renewable Energy Management and Demand Response and by PSO Algorithm (Matlab code)

Code for the paper "MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response"

Dynamic electricity carbon emission factors and prices for Europe

Smart power manager for optimized solar power usage as well as greenest and cheapest energy purchase

Home energy multi-agent recommender framework with demand response and power limit consideration.

A diploma thesis investigating the options of controlling power demand of households to reduce peaks in total power consumption in smart grids.

Demand Response Analysis Framework (DRAF)

Code repository for the paper "Data-driven modelling of energy demand response behaviour based on a large-scale residential trial".

Software for the use of Multi-agent Systems in Virtual Power Plants to realize Demand Response Mechanisms

Code for our paper Demand Response Model Identification and Behavior Forecast with OptNet: a Gradient-based Approach.

MATLAB code and data for the research article : I. Daminov, R. Rigo-Mariani, R. Caire, A. Prokhorov, M-C Alvarez-Herault, “Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime” in Energies (IF: 2.702, Q2), 2021

An open source software to model branch- and technology-specific electricity load profiles in the tertiary sector and analyse potentials of demand side response measures.

This repository contains the implementation of reinforcement learning algorithms for optimizing energy demand response in commercial buildings. The project focuses on reducing peak loads and improving energy efficiency by controlling HVAC and lighting systems using state-of-the-art RL techniques.

Hierarchical Model-Free Transactional Control of Building Loads to Support Grid Services

Dynamic Decision Making for Demand Response through Adaptive Event Stream Monitoring

Case studies of the draf demo paper