Ensemble Learning for Building Data Semantics
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Solution Overview
Problem
Existing systems for smart buildings face challenges in standardizing the representation of semantics from diverse data sources, such as sensor names and configurations, making it difficult to develop machine learning models that can effectively understand and control building systems across different environments.
Innovation Solution
The use of ensemble learning methods to combine specialized base classifiers, each trained on specific types of data, to generate a comprehensive semantic map that can interpret and utilize data from various sources, including textual metadata and time-series data, allowing for a more complete understanding of sensor types, locations, and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single machine learning model is developed to cover all patterns in buildings, then comprehensive coverage is achieved, but the model becomes overly complex and difficult to train
Solution Approach 1:
The patent divides a single complex machine learning model into multiple specialized base classifiers, each trained on specific types of building data (e.g., HVAC systems, lighting, security). These classifiers are then combined through an ensemble method to achieve comprehensive pattern coverage while maintaining individual model simplicity and trainability.
2Measurement precision
If domain experts manually map sensor names to standard vocabularies, then semantic understanding is achieved, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent replaces the manual mechanical process of domain experts mapping sensor names with an automated machine learning-based natural language processing system. The system learns semantic relationships from training data and automatically maps diverse sensor names and configurations to standardized vocabularies, dramatically reducing time and resource requirements while maintaining high accuracy.
3Measurement precision
If machine learning models are trained on building-specific data, then accuracy for that building is improved, but the model cannot be effectively applied to different buildings
Solution Approach 1:
The patent creates a universal machine learning framework that can adapt to different buildings through ensemble learning. The system trains multiple base classifiers on diverse building data during a training phase, then combines them into an ensemble model that can generalize across different building types, sizes, and configurations while maintaining the ability to capture building-specific patterns when needed.
4Quantity of substance
If diverse data sources are integrated into a single system, then comprehensive data coverage is achieved, but the system complexity increases
Solution Approach 1:
The patent segments diverse data sources into distinct processing streams, with specialized base classifiers handling different data types (sensor data, configuration files, floor plans, operational logs). Each classifier processes specific data types independently, and the ensemble method combines their outputs, achieving comprehensive data coverage while managing complexity through modular organization.
Data Source
AI summary
Described herein are systems and methods for utilizing ensemble learning methods to extract semantics of data in buildings by retrieving a plurality of data sets from a plurality of data sources associated with an automated environment; labeling a subset of the plurality of data sets by applying Natural Language Processing (NLP) on manufacturer specifications to generate a plurality of labels associated with the subset of the plurality of data sets, respectively; training a learning model on the subset of the plurality of data sets and the plurality of labels; and applying the learning model on remanding subset of the plurality of data sets to generate a semantic map indicative of semantic arrangement of the plurality of data sources associated with the automated environment.