GreenData: The Future of AI-Driven Energy Efficiency, 2026 CES Innovation Award Honoree

Greendata, a company specializing in energy data analysis and deep learning AI model development, is leading sustainable energy efficiency innovation through its groundbreaking energy management platform, GreenOS. The company’s technological excellence was recognized globally when it earned the Innovation Award at CES 2026, the world’s largest consumer electronics and IT exhibition. Moving far beyond traditional metering and monitoring, GreenOS delivers a comprehensive suite of capabilities from building correlation analysis models across countless variables affecting energy consumption, to running multi-dimensional AI simulations that optimize energy efficiency. The platform provides fully customized energy management solutions tailored to the unique environments of office buildings, campuses, hospitals, factories, and public housing. With the AI chatbot Greeny, users can instantly access essential insights without navigating complex dashboards, including Real-time energy issue diagnostics, Automated reporting, Consumption forecasting, Reduction scenario recommendations, Cost-optimization consulting. Additionally, the platform offers specialized energy reduction algorithm APIs for industrial process equipment, HVAC systems, air-handling units, and household appliances. As a member of the Carbon Accounting Association (CAA), Greendata continues to advance cutting-edge technologies for sustainable energy use, strengthening its global competitiveness.

Green OS for Home

Key Features
1. Real-Time Monitoring: Performs energy management and optimization based on real-time data. 2. Smart Home Integration: Energy management and appliance control in smart home environments. 3. Customized Optimization: Tailored efficiency optimization using AI-driven simulations.

Real-Time Monitoring System
Greendata’s advanced Real-Time Monitoring System provides AI-based anomaly detection and actionable energy reduction solutions. By leveraging continuous data streams, the system manages energy consumption with unparalleled precision, minimizing waste and maximizing operational efficiency. Users gain immediate visibility into their energy use and can take prompt corrective actions, fostering sustainable habits and empowering smarter energy decisions.

Smart Home Control
GreenData offers a next-generation smart home energy management and appliance control solution that integrates all household energy flows into a unified, intelligent control system. This integration enables users to maximize efficiency and convenience while maintaining an eco-friendly lifestyle. The platform enhances visibility into household energy behavior and supports effective, sustainable energy practices through intuitive monitoring and smart automation.

Customized Energy AI Management
Using advanced AI simulations, Greendata provides tailor-made energy management strategies that consider user-specific factors such as building type, operational behavior, environment, and geographical conditions. By analyzing detailed consumption patterns, the AI identifies the most effective cost-saving pathways. This personalized approach helps maximize efficiency, align with sustainability goals, and support long-term cost reduction enabling smarter and more sustainable energy usage for every customer.

Green OS

Key Features
1. Deep Learning Solutions: Deep learning-based energy management tailored to diverse facility environments, with data-driven equipment power analysis and optimization strategies. 2. Sustainable Strategy: Sustainable energy management strategy using big data and machine learning. 3. Enhanced Efficiency: Increased energy efficiency through various AI research, such as NILM.

Deep Learning Energy Management Solution
GreenOS leverages deep learning models to analyze and predict energy consumption patterns, enabling precise and actionable energy reduction recommendations. By tailoring its deep learning–based algorithms to the characteristics of each facility type, the platform maximizes savings and operational efficiency. Real-time AI reports highlight current consumption, identify anomalies, and propose optimized reduction scenarios. Through conversational interaction, Greeny provides users with seamless access to bespoke energy solutions.

Power Analysis and Optimization Strategy
Through large-scale data analysis, GreenOS performs granular, equipment-level power consumption profiling and provides targeted optimization strategies. With the Agentic AI Greeny, users can quickly retrieve operational data to enhance equipment performance and reduce energy costs. AI-driven reports detail usage patterns, energy-saving opportunities, and predictive consumption trends, enabling data-backed decision-making for facility operations.

Sustainable Energy Management Strategy leveraging Big Data and Machine Learning
GreenOS delivers long-term, sustainable energy strategies powered by big-data analytics and machine learning algorithms. These insights include consumption forecasts, environmental impact reduction plans, and comprehensive energy-saving scenarios. Agentic AI Greeny provides all required energy intelligence in real time, ensuring that sustainable energy management becomes both practical and effortless.

HVAC System Optimization
Through extensive AI simulations across varying environmental conditions, GreenOS identifies optimal HVAC operating parameters that balance comfort with maximum energy efficiency. The system continuously monitors operational performance, enabling instant adjustments when needed. Using the Greeny chatbot, users can easily review HVAC data, optimize system settings, and maintain peak performance while minimizing energy costs.

Deeper Analysis with Non-Intrusive Load Monitoring (NILM)
NILM (Non-Intrusive Load Monitoring) is an advanced technique that accurately identifies the energy consumption of individual devices or equipment using only a single main meter signal. This eliminates the need for deploying sensors on every appliance significantly reducing installation cost and complexity. NILM provides precise, device-level insights into sources of energy waste across factories, commercial buildings, and residential units revealing exactly “what to reduce and when.” This granular intelligence supports accurate consumption forecasting and optimized reduction scenario modeling, ultimately boosting overall energy management efficiency.

Sustainable Efficiency with MLOps
Energy efficiency models must adapt continuously to changing real-world conditions seasonal variations, equipment aging, and evolving user behaviors. MLOps automates and streamlines this lifecycle. Automated Model Retraining: Deep learning models are continuously retrained and redeployed based on newly collected data to maintain optimal prediction accuracy over time. Rapid Field Adaptation: When new equipment is introduced or operational conditions shift, the AI models are updated swiftly to reflect actual site conditions, ensuring agile energy management. Sustainable, Long-Term Management: MLOps enables all customer environments (factories, resorts, residential complexes) to receive continuously updated energy-saving strategies, supporting long-term cost reduction and environmental stewardship.