Machine Learning Model for Building Energy Renovation Verification
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Solution Overview
Problem
Current energy performance diagnosis and evaluation of buildings are costly and time-consuming, and lack a systematic method for managing data before and after renovation, leading to varying results depending on expert opinion and user conditions.
Innovation Solution
An energy improvement learning method and system that generates a learning model using building characteristic information, diagnosis results, and renovation data to provide energy-saving schemes for target buildings, reducing costs and improving reliability by integrating data management and machine learning for predictive analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy performance diagnosis and evaluation are performed by qualified organizations or experts, then diagnosis accuracy and reliability are improved, but economic costs and time consumption increase significantly
Solution Approach 1:
The system performs preliminary energy performance diagnosis and evaluation through automated data collection and analysis before actual renovation work begins. By pre-assessing building characteristics, energy consumption patterns, and potential improvement measures, the system enables early verification of renovation effectiveness without requiring time-consuming post-renovation re-diagnosis by experts.
Solution Approach 2:
The system creates a virtual model or digital twin of the building that replicates its energy performance characteristics. This virtual copy allows for simulation and evaluation of different renovation scenarios, enabling accurate prediction of energy savings without requiring physical intervention or expert site visits, thereby reducing both time and cost while maintaining reliability.
2Reliability
If energy performance diagnosis is performed by specialized experts, then diagnosis quality is improved, but economic costs increase
Solution Approach 1:
The system enables automated self-diagnosis of building energy performance by collecting and analyzing data from existing building management systems, meters, and sensors. The automated analysis engine processes this data to generate energy performance assessments and renovation recommendations, eliminating the need for expensive expert services while maintaining consistent diagnosis quality through standardized algorithms.
Solution Approach 2:
The system replaces the mechanical process of expert manual assessment with an automated computational system. Instead of relying on human experts to physically inspect buildings and manually analyze data, the system uses automated data collection, processing, and analysis algorithms to perform the same function more efficiently and at lower cost, while maintaining or improving diagnostic consistency.
3Adaptability or versatility
If renovation decisions are made based on expert opinion and user conditions, then adaptability to individual cases is improved, but systematic data management and verification are worsened
Solution Approach 1:
The system provides a universal platform that handles multiple functions including data collection, analysis, renovation recommendation, and verification across different building types and user conditions. By creating a standardized yet flexible framework that can adapt to various building characteristics and user requirements, the system maintains adaptability while systematically managing all related data through integrated databases and processing workflows.
Solution Approach 2:
The system implements closed-loop feedback by continuously collecting data before and after renovation, comparing actual results with predicted outcomes, and using this information to improve future assessments. This systematic feedback mechanism enables verification of renovation effectiveness while adapting to individual building characteristics and user conditions, creating a self-improving system that reduces data management complexity over time through learned patterns.
Data Source
AI summary
A method and system for energy improvement verification of buildings. An energy improvement learning method includes receiving building characteristic information related to an existing building constructed in the past and a diagnosis result of energy diagnosis performed based on the building characteristic information, receiving a result of renovating the existing building using energy saving schemes suitable for the diagnosis result, and generating an energy improvement learning model for the existing building by learning the building characteristic information and the renovation result.