Eco-friendly building credit calculation system

The eco-friendly building credit calculation system addresses the challenge of predicting energy saving effects and construction costs by using big data and AI to estimate LEED scores and costs, enhancing energy efficiency and LEED ratings through precise analysis.

KR103001016B1Active Publication Date: 2026-08-05박재용
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Patent Information

Application Number
KR1020240036621
Authority / Receiving Office
KR · KR
Patent Type
Patents
Current Assignee / Owner
Filing Date
2024-03-15
Publication Date
2026-08-05
Estimated Expiration
2044-03-15

AI Technical Summary

Technical Problem

Existing methods struggle to accurately predict energy saving effects and construction costs in building energy efficiency improvements due to variations in energy saving control methods and building environments, making it difficult to achieve LEED certification.

Method used

An eco-friendly building credit calculation system utilizing big data analysis, integrated artificial intelligence, and environmental measurement devices to estimate LEED credit scores and construction costs based on building specifications, considering administrative district, surrounding environment, and energy consumption sources.

Benefits of technology

Enables precise prediction of building construction costs reflecting energy efficiency, improving energy efficiency ratings, and determining energy saving rates and LEED credit scores through objective big data analysis and environmental factor consideration.

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Abstract

The present invention relates to an eco-friendly building credit calculation system implemented to predict building construction costs reflecting building energy efficiency according to building specifications, comprising: a building database server that receives at least one building specification from at least one building, receives it from the construction company or building owner of the building at the time of construction, and stores it in a database; a user terminal that receives big data information reflecting the building specification, administrative district associated with the administrative address, and surrounding environment from the building database server; and a credit calculation server that estimates the LEED (Leadership in Energy and Environmental Design) credit score and the construction cost of the building through big data analysis based on the building specification received from the user terminal.
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