Carbon Accounting
SaaS solutions for corporate and municipal emissions monitoring, from providing emission factor APIs to custom user interface to address various carbon accounting needs.
Energy Management Solution
Cloud-based SaaS solution to achieve energy savings by identifying and optimizing hot spots for a proactive carbon reduction.
Decarbonization AI
AI services specifically designed to achieve carbon neutrality, including renewable energy integration and energy optimization at the device and facility levels.
Highlights
We provide a one-stop solution for carbon management, from emissions calculation to reduction.
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Emission factors provided as APIs for quick and easy deployment: 41,500+ factors listed.
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Emission factors database with global coverage, featuring the world’s most extensive coverage of Asia-Pacific and the manufacturing sector.
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Integrated classification system to cover emission activities by country
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Highly reliable emission factor data that underwent row-by-row inspection
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User-friendly information and data (e.g., currency, unit, and data source)
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Real-time emissions query via API
Features
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Carbon Accounting
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Energy Management Solution
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Cloud-based SaaS to address energy reduction needs of manufacturers
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Carbon emissions and costs reduction of day-to-day operations by leveraging IoT, digital twinning, and energy optimization solutions
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Automated data collection and analytics for on-site utility asset management
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AI-powered energy consumption, operations, and maintenance data analytics to achieve energy efficiency and to assist with decision-making
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Collect and integrate data with 30% less cost and 20% less time than conventional system integration solutions without downtime
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Decarbonization AI
Decarbonization AI service is provided via Thingspire’s MLOps that performs three core functions: prediction for energy consumption and production, predictive maintenance and fault diagnosis, and improved integration of renewable energy. The existing market solutions have separated operators and users, whereas our MLOps enables users to customize their operations to meet their needs, supported by four functions:
Low Code
Dashboard
Flow Chart
Chatbot
Use Cases
INTEGRICT CEMS is a Digital Twin-based end-to-end energy solution in development for Hyundai Electric of HD Hyundai Group. It is designed to systematically manage utility assets in the factory setting for practical energy efficiency and provides automated solutions for collecting and analyzing data within the platform. By centrally managing energy consumption, operation, and maintenance on the cloud, carbon emissions are calculated at ease in support of energy efficiency and managerial decision-making. CEMS enables data collection and integration at 30% less cost and 20% less duration than the existing that of the market benchmark.
Virtual Utility Plant (VUP) is a novel concept which builds upon core principles of Virtual Power Plant (VPP) to extend the same methodology across a wider set of utilities for industrial complexes. The concept was implemented to maximize energy utilization through transactions between prosumers for various forms of energy, consisting of Water / (Compressed) Air / Gas / Electricity / Steam (W-A-G-E-S) within the industrial complex, via an integrated management system following smart industrial complex protocol. The project achieved its objective of reducing steam usage by 7%, compressed air by 10%, and infrastructural installation costs by 15%, verified through third party testing.
The forgoing system supports the automation of inventory buildup as well as emissions calculation for Gwangju Metropolitan City as it strives for Net Zero. In addition to carbon accounting at the community scale, the system is designed to manage the performance of individual energy-intensive sites within the reporting boundaries. As part of our R&D&I efforts, we will conduct a proof-of-concept by simulating a localized emission trading system for participating organizations upon voluntary reduction of their carbon footprint.
The platform is being developed with the ultimate goal of promoting carbon neutrality and RE100 by integrating renewable energy sources to complex-wide energy management system at Banwol-Sihwa, a government designated Smart Green Industrial Complex. On an annual basis, our objective is to assist the implementation sites achieve a total of 23,214 MWh of local power generation from renewable sources, save additional 2,500 MWh of energy through various green energy initiatives, and further reduce 2,612 tons of carbon emissions through small-scale carbon credits purchases.
The Thingspire MLOps-based steam trap predictive maintenance system proactively detects condensate discharge failures in steam systems to maintain steam quality and save energy. We have applied the system in a variety of industries, including metal rolling, semiconductor, dyeing, food and beverage processing, and others, demonstrating average energy cost savings of more than 5%.
The Thingspire MLOps-based Motor Optimal Control System is a solution which optimizes power usage by automatically adjusting motor loads using a dedicated AI algorithm. The system improves the power efficiency of air compressors, chillers, etc., which are widely used by manufacturers to reduce energy costs. By utilizing in-depth training and optimization technology, it analyzes the operating status of equipment (loads) in real time and automatically sets optimal operating conditions, thereby reducing energy use, and contributing to sustainable energy management of our clients.
Thingspire MLOps-based PV power generation prediction solution collects and processes various data sources, including location-based data (latitude, longitude, atmospheric volume, etc.), climate data (temperature, cloud cover, visibility, humidity, barometric pressure, etc.), weather forecast, and power generation equipment data (inverter capacity, power output, etc.) to predict power generation through advanced machine learning models. Our solution comes with an hourly prediction accuracy of over 92% for the next day, improving the economics and sustainability of renewable energy plants.
This solution uses thermal imaging from drones to detect damage and contamination on solar panels to maintain optimal power generation through maintenance support. Utilizing object detection technology to identify the type of damage on the solar panels, our solution diagnoses abnormalities in the panels, which is best suited for companies interested in improving the operational efficiency of solar power plants at the average rate of 3.3%.
This is an ongoing project to diagnose wind turbine blade defects using drone images to perform predictive maintenance. The drone detects the direction of the wind turbine's nacelle and the precise rotation angle of the blades on a dynamic, real-time basis to help the drone adjust itself to face the blade directly. The solution then analyzes the images taken by the drone to precisely detect damage such as wear, corrosion, dents, and breaks. This supports the efficient maintenance of wind turbines and contributes to minimizing downtime, improving safety and operational efficiency.
Offerings
Full Coverage: from emissions calculation to reduction
Carbon Management: reduce emissions and operating costs
Enhanced productivity
Analytics: bird’s eye view on ESG decision-making
Automate ESG disclosure proactively and management
Secure global industrial competitiveness in a low-carbon & green economy