Building automation method and system
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Solution Overview
Problem
Existing building automation systems face challenges in accurately differentiating between human behavioral patterns and automated processes, leading to inefficiencies in energy management and comfort control, particularly in distinguishing between absence and presence, and various activities such as sleep and wake states.
Innovation Solution
A building automation system that utilizes time histories of consumption patterns from standard infrastructure resources, including internet gateways and home entertainment devices, to filter out automated events and differentiate between human presence and absence by averaging data over several days, employing statistical methods like PCA and fuzzy logic to recognize occupancy patterns and adapt temperature settings accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If building automation systems use standard infrastructure meters and sensors to detect occupancy patterns, then system cost is reduced, but the ability to accurately differentiate between human behavioral patterns and automated processes deteriorates
Solution Approach 1:
The system segments the detection task by analyzing multiple different data sources (electricity consumption, water consumption, gas consumption, temperature, humidity) separately and then combining them through data fusion to achieve accurate occupancy detection while using only standard infrastructure components
Solution Approach 2:
The system merges data from multiple standard infrastructure meters and sensors to create a comprehensive occupancy detection system, combining electricity, water, gas, and environmental sensor data to differentiate between human presence and automated processes
2Reliability
If the system averages time histories of consumption patterns to improve reliability, then data reliability is improved, but the time required for pattern recognition increases
Solution Approach 1:
The system performs preliminary averaging of time histories of consumption patterns before pattern recognition, preparing refined data in advance to improve the reliability of occupancy detection while establishing a systematic approach to data processing
3Measurement precision
If the system uses multiple data sources from standard infrastructure, then measurement coverage is improved, but data processing complexity increases
Solution Approach 1:
The system introduces data fusion as an intermediary process that integrates multiple data sources from standard infrastructure (electricity, water, gas, temperature, humidity sensors) into unified occupancy patterns, managing the complexity of processing diverse data streams through a coordinated multi-step process
Data Source
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AI summary
A building automation system. A method for automating a building, the process comprising the steps of acquiring (1) at least one data time history (1a, 1b, 1c, 1d, ... 1n) from a sensor or from a meter, averaging (4) the at least one data time history, arranging the at least one averaged (4) data time history (1a, 1b, 1c, 1d, ... 1n) into at least one occupancy pattern (5), wherein the at least one occupancy pattern (5) covers a given time span, determining at least one set point from said occupancy pattern (5), feeding the at least one set point into a system (14) for heating, ventilation, air-conditioning, characterized in that the at least one data time history (1a, 1b, 1c, 1d, ... 1n) is acquired from an element of standard infrastructure.