General Overview
RL-Energyplus is an advanced AI-powered platform designed to optimize energy consumption in buildings using Reinforcement Learning (RL). By leveraging AI-driven automation, it ensures efficient climate control while reducing costs and environmental impact. Built on a collaborative ML platform, RL-Energyplus integrates simulation, data analysis, and agent-based learning for intelligent energy management.
Watch the platform intro we submitted to the NTT Group Sustainability Conference 2023.
Platform & Technology
The RL-Energyplus platform industrializes AI model training for real-world applications by combining Deep Reinforcement Learning (DRL) with EnergyPlus simulations to dynamically optimize HVAC settings.
Key features include:
- Integrated Environment: Seamlessly combines RL frameworks, simulation engines, and experiment tracking tools.
- MLOps Capabilities: Ensures scalability with automated model versioning and logging.
- Scalability & Automation: Deployable in cloud environments like AWS Fargate for large-scale experiments.
Projects
ADDA (Auditorium Diputación Alicante)
- Goal: Optimize temperature & humidity to maintain acoustic conditions by preserving instrument tuning.
- Approach: RL agents trained on EnergyPlus models adjust cooling, heating, and airflows dynamically.
- Key Learnings: Identified optimal control strategies while addressing HVAC system challenges.
- Partners: Universidad de Alicante, Centro de Inteligencia Digital Alicante (CENID)
San Juan del Hospital (Valencia)
- Goal: Optimize CO2, temperature, and humidity to prevent material degradation in a historical site.
- Solution: RL-driven control of ventilation and HVAC systems to regulate climate conditions.
- Outcome: Demonstrated the feasibility of AI-driven climate control in heritage buildings.
- Partners: Universidad de Alicante, Centro de Inteligencia Digital Alicante (CENID)
Founders
Vicente Castillo
Vicente Castillo is an architect, product development specialist, and business strategist. Currently a Specification Sales Manager at Rain Bird, he helps clients optimize irrigation solutions. With a career spanning architecture, industrial design, and entrepreneurship across Sweden, the UK, Switzerland, and Brazil, Vicente combines product design expertise, intellectual property, and data-driven decision-making with his passion for art, painting, and music.
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Sergio Ortiz
Sergio Ortiz is a Chief Architect within the Core Team of the Global Assets Unit at NTT DATA, Inc., where he leads the technical definition and evolution of the Agentic Enablement Platform, driving the transition toward an AI-first, agent-enabled product ecosystem. With deep experience in enterprise architecture, platform engineering, DevOps practices, and AI-enablement models, Sergio specializes in cloud-native architectures, automation, product engineering, and the governance required for production-grade AI systems.
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