Targeted demand response for flexible energy communities
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Updated
Jul 16, 2024 - Python
Targeted demand response for flexible energy communities
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.
Case studies of the draf demo paper
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 online web application that enables you simulate solar pre-cooling for a range of exsiting buildings in Australian building stock.
Quantify geospatial heating flexibility potential based on heating consumption data
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.
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
Home energy multi-agent recommender framework with demand response and power limit consideration.
Work done while interning at IIT Bombay
SimulationX
Code repository for the paper "Data-driven modelling of energy demand response behaviour based on a large-scale residential trial".
Hierarchical Model-Free Transactional Control of Building Loads to Support Grid Services
Home appliance controller learn from resident negative feedback
GoEpexSpot aims to be a comprehensive API client for fetching Day-ahead EPEX SPOT prices using Golang (Go).
This is an attempt to produce load forecast for an electric grid using historic load and weather data
Smart power manager for optimized solar power usage as well as greenest and cheapest energy purchase
Code for our paper Demand Response Model Identification and Behavior Forecast with OptNet: a Gradient-based Approach.
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