Building Energy Analysis Using Balance Points and Exception Ranking
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Solution Overview
Problem
Current energy monitoring systems in buildings lack the capability to provide accurate energy data analysis and control, leading to inefficiencies in energy consumption and costs.
Innovation Solution
An energy analysis system that includes a processor, communication interface, and memory for analyzing energy data, determining baseline and actual consumption, and identifying exceptions, with the ability to control building systems through a network operations center, using balance point determination logic and comparison logic to optimize energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If energy monitoring systems obtain and analyze energy data from individual pieces of equipment, then equipment-level energy data is available, but accurate building-level energy expenditure analysis cannot be determined
Solution Approach 1:
The system segments energy data collection and analysis into two levels: equipment-level monitoring (individual pieces of equipment) and building-level analysis (aggregate energy expenditures). This segmentation allows the system to maintain simple equipment sensors while implementing sophisticated aggregate analysis at the building level, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The system introduces an intermediary analysis layer that aggregates equipment-level energy data to produce building-level energy expenditure analysis. This intermediary layer translates simple equipment measurements into meaningful building-wide insights without requiring complex sensors at the equipment level, thereby improving measurement precision while maintaining device simplicity.
2Loss of energy
If building systems are controlled to optimize energy consumption, then energy savings are achieved, but system complexity increases
Solution Approach 1:
The system implements feedback control by continuously monitoring energy data, analyzing expenditures, and using these insights to optimize building system operations. The feedback loop enables energy optimization through data-driven decisions rather than complex real-time control algorithms, reducing energy loss while avoiding excessive system complexity.
3Productivity
If detailed energy analysis is performed to identify exceptions and optimize consumption, then energy efficiency improves, but data processing requirements increase
Solution Approach 1:
The system performs preliminary energy analysis by establishing baseline energy expenditures and comparing actual consumption against these baselines. This preliminary action identifies exceptions and optimization opportunities in advance, enabling proactive energy efficiency improvements without requiring intensive real-time data processing, thus improving productivity while minimizing time loss.
Data Source
AI summary
An energy analysis system provides valuable input into building energy expenditures. The system assists with obtaining a detailed view of how energy consumption occurs in a building, what steps may be taken to lower the energy footprint, and executing detailed energy consumption analysis. The analysis may include, as examples, a balance point pair analysis to determine either or both of a heating balance point and a cooling balance point, an exception rank analysis to identify specific data (e.g., energy consumption data) in specific time intervals for further review, or other analysis. The system may display the analysis results on a user interface.