EcoSynergy AI | Sustainable Building Intelligence

About Our Project

EcoSynergy AI is a revolutionary platform that uses hybrid AI techniques to optimize energy usage in industrial buildings.

Transforming Building Management with AI

Buildings are responsible for approximately 40% of energy use and 36% of CO₂ emissions in the EU. Traditional building management systems rely on static schedules or basic rule-based controls that do not adapt to real-time conditions.

Our cloud-enabled platform uses cutting-edge hybrid AI techniques to create self-optimizing, sustainable buildings that can significantly reduce energy consumption and carbon emissions without compromising comfort or productivity.

Our Netherlands-based startup is developing a solution that will serve as an intelligent "brain" for smart building management, dynamically controlling systems to reduce consumption and costs.

Our Project Goals

We're on a mission to revolutionize how industrial buildings manage energy consumption through advanced AI techniques.

Reduce Energy & Emissions

Achieve 20-25% reduction in HVAC and lighting energy
Optimize without compromising comfort or production
Validate through rigorous A/B testing
Develop sustainability KPI dashboard

Innovate Beyond State-of-the-Art

Combine reinforcement learning, genetic algorithms, and MILP
Develop proprietary integration architecture
Create transfer learning framework
Advance to TRL 5+ with industrial validation

Intelligent Decision Support

Create an "autonomous sustainability advisor"
Implement explainable AI techniques
Develop natural language interface
Support both automated and human-in-the-loop modes

Enable Industrial Adoption

Design modular, interoperable architecture
Create low-friction implementation paths
Develop scalable cloud infrastructure
Build reference implementation for EU deployment

Drive Industry Standards

Share anonymized performance data
Develop open benchmarks
Document sustainable AI best practices
Create leadership case studies

Our Hybrid AI Approach

We combine multiple AI techniques into a unified system for optimal building management.

Reinforcement Learning

Our deep reinforcement learning agents learn optimal control policies for dynamic building systems. They continuously adjust controls to minimize energy use while maintaining comfort and operational requirements.

Genetic Algorithms

We employ genetic algorithms for higher-level planning and optimization tasks, finding the best combination of equipment schedules, temperature set-points, and configuration parameters.

Mixed-Integer Linear Programming

For decisions best solved as constrained optimization problems, we use MILP to handle discrete on/off decisions, load shifting, and ensuring hard constraints are never violated.

Our Team

Meet the innovators behind EcoSynergy AI's groundbreaking sustainable building technology.

Can Olmezoglu

Project Lead & AI Architecture

Research Engineer at Fraunhofer with expertise in AI/ML solutions for industrial challenges. MSc student in Computer Science (Machine Learning) at Georgia Tech. Leads overall system architecture and reinforcement learning components.

Ibrahim Teymurlu

Data Science & Optimization

Research Assistant at TU Eindhoven specializing in operations research and optimization algorithms. Leads genetic algorithms and linear programming components with expertise in heuristic approaches and constraint optimization.

Salih Eren Yuceturk

Full-Stack Development & UI/UX

Software Developer at Axxemble with strong experience in web application development. Leads dashboard development, visualization systems, and the natural language interface for our building optimization platform.

Karam Altabbaa

Stakeholder Engagement & Project Coordinator

Psychology student at the University of Twente who leads user research to shape our explainable AI interface. Coordinates project tasks, timelines, and stakeholder communications. Applies his psychology expertise to enhance user experience and adoption, and drives marketing and outreach across the ELIAS ecosystem.

Yigit Bezek

IoT & Hardware Integration

Creative Technology student with extensive experience in microcontrollers, sensors, and smart systems. Handles sensor integration, IoT framework development, and physical interfaces between AI systems and building equipment.

Our Working Philosophy

Integrated Intelligence

Rather than siloed AI approaches, we've created a cohesive system where multiple techniques work in concert, monitoring building performance from different angles simultaneously.

Practical Innovation

We balance cutting-edge research with pragmatic implementation, focusing on solutions that can be deployed quickly and show measurable results within months, not years.

Transparent Technology

Our AI doesn't operate as a black box—we design systems that clearly explain their decisions, helping facility managers understand and trust the optimization process.

Adaptive Learning

Our platform continuously improves over time, learning from both successes and challenges to deliver increasingly optimized performance for each unique building environment.

Our Partners & Supporters

We're proud to collaborate with organizations that support our innovation journey.

Novel-t Incubator Program

University of Twente Entrepreneurial Challenge

As participants in the Novel-t incubator program, we receive mentorship, resources, and networking opportunities to accelerate our growth. Through the University of Twente Entrepreneurial Challenge, we're developing our business model and connecting with potential industry partners.

Sustainable Building Intelligence

Using hybrid AI to transform industrial buildings into self-optimizing, energy-efficient environments