A Pytorch DQN and DDPG implementation for a smart home energy management system under varying electricity price.
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Updated
Feb 8, 2023 - Jupyter Notebook
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
A diploma thesis investigating the options of controlling power demand of households to reduce peaks in total power consumption in smart grids.
Home energy multi-agent recommender framework with demand response and power limit consideration.
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".
Code for our paper Demand Response Model Identification and Behavior Forecast with OptNet: a Gradient-based Approach.
Software for the use of Multi-agent Systems in Virtual Power Plants to realize Demand Response Mechanisms
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.
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
Dynamic Decision Making for Demand Response through Adaptive Event Stream Monitoring
Hierarchical Model-Free Transactional Control of Building Loads to Support Grid Services
Case studies of the draf demo paper
Home appliance controller learn from resident negative feedback
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