Building Occupancy Estimation for Demand Response Dispatch Priority
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Solution Overview
Problem
Current methods for determining occupancy in buildings are inadequate for real-time analysis and energy management, as they rely on subjective techniques like scaling and 'eyeballing', which are inefficient and inaccurate, especially when occupancy changes significantly.
Innovation Solution
A system that uses energy consumption and outside temperature data to automatically determine occupancy levels, employing a network operations center with computer code to process energy consumption streams and generate occupancy levels, which are then used to optimize demand response programs and manage energy use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated occupancy determination using energy consumption data is implemented, then measurement precision and productivity are improved, but device complexity increases
Solution Approach 1:
The patent uses energy consumption data as an intermediary indicator to indirectly determine occupancy levels. Instead of directly counting occupants, the system measures energy consumption from building systems (HVAC, lighting) and correlates it with occupancy patterns. This intermediary approach improves measurement precision while avoiding the complexity of direct occupancy sensing infrastructure.
Solution Approach 2:
The system creates a virtual model of occupancy by processing and analyzing energy consumption data streams. Rather than physically monitoring each occupant, the patent generates an occupancy representation through computational analysis of existing energy usage patterns, reducing the need for complex physical sensing infrastructure.
2Productivity
If real-time occupancy analysis is implemented, then productivity and response time are improved, but use of energy increases
Solution Approach 1:
The system performs preliminary analysis by pre-processing energy consumption data and establishing baseline occupancy patterns before real-time decision-making is required. By preparing occupancy models in advance and using historical data to predict future occupancy, the system reduces the computational burden during real-time operations, thereby lowering real-time energy consumption while maintaining high productivity.
3Productivity
If automated dispatch prioritization is implemented, then productivity and demand response effectiveness are improved, but device complexity increases
Solution Approach 1:
The system simplifies dispatch prioritization by changing the key parameter from complex multi-factor analysis to occupancy-level-based prioritization. Buildings are prioritized for demand response actions primarily based on their determined occupancy levels, with simpler secondary considerations. This parameter focus reduces control system complexity while maintaining demand response effectiveness and productivity.
Data Source
AI summary
A method for prioritizing a demand response program event based on occupancy for one or more buildings of one or more building types participating in a demand response program, the method comprising: receiving energy consumption and outside temperature streams Ei(h,T) corresponding to a portion of the one or more buildings, and receiving and employing occupancy components for each of the one or more buildings within the portion to process the streams, and generating occupancy levels corresponding to the one or more buildings within the portion, and assigning the occupancy levels to remaining ones of the one or more buildings not in the portion; and optimizing execution of the demand response program event by employing the occupancy components to prioritize dispatch messages to the one or more buildings to achieve objectives of the demand response program event.