Lower peak-time energy consumption by up to 30% and extend the lifespan of your HVAC equipment through predictive maintenance, preventing costly breakdowns.
02/03
Minimize peak demand & maintenance costs
Thermolio aims to cut HVAC-related carbon emissions by 35% and enhance indoor air quality, boosting occupant health and productivity.
03/03
Lower Carbon Footprint and Improve Air Quality
35%-50% of overall energy consumption in commercial buildings is from HVAC systems. Our AI-powered solution reduces this by optimizing your existing setup, leading to substantial savings on energy bills
01/03
Reduce energy HVAC costs upto 60%
Thermolio
How it works:
01/06
Connect seamlessly with your existing smart thermostats and HVAC systems. Quick installation, minimal disruption.
02/06
Monitor real-time data from your HVAC systems, including temperature, humidity, occupancy, and weather conditions.
03/06
Analyze data with AI to adjust HVAC settings dynamically, maximizing efficiency and minimizing energy use.
04/06
Adapt:
Automatically adjust to changes in occupancy and weather, ensuring comfort while reducing energy consumption.
05/06
Access real-time dashboards with insights on energy use, air quality, and savings.
06/06
Continuously learn and refine the algorithm for ongoing energy optimization and efficiency gains.
Backed up by years of research & advanced machine learning algorithms
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AI-Based MPC Controllers optimize HVAC systems by using AI models like TF and LSTM for real-time, data-driven adjustments. This reduces energy consumption by 25% compared to traditional PID controllers, making HVAC operations more efficient.
AI-Based MPC Controllers for Energy-Efficient HVAC Systems
AI-Based MPC Controllers optimize HVAC systems by using AI models like TF and LSTM for real-time, data-driven adjustments. This reduces energy consumption by 25% compared to traditional PID controllers, making HVAC operations more efficient.
MPC-Based Efficient Energy Control and Cost Estimation of HVAC in Buildings
AI-Based MPC Controllers optimize HVAC systems by using AI models like TF and LSTM for real-time, data-driven adjustments. This reduces energy consumption by 25% compared to traditional PID controllers, making HVAC operations more efficient.
WSN-Based Data-Driven Digital Twin for Energy Efficient HVAC Systems