A Python-based building energy modeling (BEM) tool designed to model flexible loads in residential buildings

Model predictive control(mpc), demand response and energyhub modeling in R

A digital companion to the research paper "Utility-Based Context-Aware Multi-Agent Recommendation System for Energy Efficiency in Residential Buildings", by Valentyna Riabchuk, Leon Hagel, Felix Germaine, and Alona Zharova

High-concurrency sale microservices with Redis, RabbitMQ, load shifting

Agent-based Coordination for Capacity and Energy Sharing Societies (ACCESS)

MoMeEnT Adaptive Survey is a Flask-based web app for model-driven adaptive surveys, integrating real-time feedback with simulation models. It explores load-shifting in energy consumption, helping users assess cost, carbon impact, and grid effects based on their behavior. Runs locally for easy testing.

Home Assistant custom integration for multi-source electricity price timelines, device runtime planning, and learned consumption profiles.

Simulating success rates for a campaign encouraging consumers to shift their electric vehicle charging from peak to off-peak hours to help alleviate grid strain.

Python demand-response simulator. Shifts 24 flexible city loads against hourly Nord Pool prices to flatten a Nordic city's evening peak. Built at Urban Circular Hack Helsinki 2025.