Dynamic electricity carbon emission factors and prices for Europe

Demand Response Analysis Framework (DRAF)

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

Targeted demand response for flexible energy communities

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

OpenADR-2B-PyServer is a free, open-source, and secure implementation of an OpenADR 2.0B server written in Python. Utilizing the OpenLEADR library, this project aims to provide a robust and reliable platform for Automated Demand Response (ADR) solutions.

GoEpexSpot aims to be a comprehensive API client for fetching Day-ahead EPEX SPOT prices using Golang (Go).

Quantify geospatial heating flexibility potential based on heating consumption data

Repository of FlexDR application developed within I-NERGY project.

Paper on estimating hourly demand side responses to intermittent electricity prices for the seminar course Energy Economics at UCPH

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

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

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

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

an online web application that enables you simulate solar pre-cooling for a range of exsiting buildings in Australian building stock.

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

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

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

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