Building Improvement Targeting System Using Machine Learning Scoring
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Solution Overview
Problem
Service providers face difficulties in identifying which buildings would benefit from improvements, such as energy-saving measures or retrofitting, due to the lack of effective data analysis and targeting methods for large groups of buildings in a geographic area.
Innovation Solution
A system that analyzes building data from various sources, including public data, satellite imagery, and sensor data, to generate scores indicating potential improvements, and selects buildings for targeted recommendations and improvements based on these scores, using machine learning to update scoring rules and deploy operational settings to control algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If service providers manually analyze building data to identify improvement opportunities, then they can provide personalized recommendations, but the process becomes time-consuming and inefficient for large groups of buildings
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computer-based systems that use machine learning algorithms and data processing to analyze building information, generating improvement opportunity scores without human intervention in the actual analysis process
Solution Approach 2:
The system enables buildings to effectively 'self-report' their improvement needs through automated data collection from various sources (building management systems, utility companies, sensors), allowing the system to autonomously identify and score improvement opportunities without requiring active participation from building owners
2Measurement precision
If service providers collect and analyze detailed building data from multiple sources, then they can improve identification accuracy, but the system complexity increases
Solution Approach 1:
The patent creates a universal scoring system that can process multiple types of building data (energy consumption, building characteristics, weather data, utility information) through a single unified machine learning model, allowing the same system infrastructure to handle diverse data sources without requiring separate analysis systems for each data type
Solution Approach 2:
The system introduces intermediate data processing layers including data normalization, feature extraction, and standardized scoring mechanisms that mediate between raw diverse data sources and the final improvement recommendations, simplifying the integration of multiple data sources through standardized intermediate representations
3Loss of energy
If service providers implement comprehensive building improvement programs, then energy savings and sustainability improve, but the difficulty of selecting target buildings from large groups increases
Solution Approach 1:
The patent transforms the building selection problem by changing the parameters used for evaluation - instead of manually reviewing multiple building characteristics, the system calculates a single composite 'improvement opportunity score' that aggregates multiple factors (energy consumption patterns, building age, climate zone, utility rates) into one measurable parameter that ranks buildings by priority
Solution Approach 2:
The system segments the large group of buildings into priority tiers based on their improvement opportunity scores, allowing service providers to focus on high-priority segments first while maintaining the capability to evaluate the entire portfolio, thereby making the selection process manageable through hierarchical segmentation
Data Source
AI summary
A system can operate to receive building data for a plurality of buildings from one or more data sources. At least one building of the buildings can include building equipment. The system can operate to generate scores based on the building data for the buildings, the scores can indicate a level of potential building improvements for the buildings. The system can operate to select the building of the buildings based at least in part on the scores. The system can operate to perform an operation based at least in part on the score to generate data to improve control of the environmental condition of the building.