Networked Building Energy Optimization via Data Sharing
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Solution Overview
Problem
Existing building energy-saving systems rely heavily on the knowledge and skill of individual operators, and there is no objective method to determine the optimal energy-saving potential for a building, as each building has unique environmental conditions and structures, leading to inefficient energy-saving practices.
Innovation Solution
A unit and system that utilize sensors, communication networks, and optimization algorithms to identify and optimize energy-saving conditions across multiple buildings, allowing for the sharing of energy-saving data and simulation-based optimization to achieve optimal energy balance without relying on individual expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individual buildings are managed independently with traditional methods, then each building can be operated with simple independent control, but energy-saving effectiveness depends on operator knowledge and skill, leading to insufficient overall energy saving
Solution Approach 1:
The patent merges multiple independent building management systems into a networked system where buildings share operational data and control strategies. The central control unit collects information from sensors across multiple buildings and distributes optimized control signals, transforming isolated management into coordinated operation to achieve superior energy savings.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from buildings is collected, analyzed, and used to adjust operational parameters. The control unit receives real-time information about energy consumption, environmental conditions, and operational status, then automatically modifies facility operations to optimize energy efficiency based on actual performance data.
2Device complexity
If building management relies on operator knowledge and skill, then simple individual building control is maintained, but energy-saving potential cannot be objectively determined due to unique environmental conditions and building structures
Solution Approach 1:
The system enables buildings to automatically determine their own optimal energy-saving strategies by comparing their operational data with aggregated data from other buildings. The control unit autonomously analyzes sensor information, identifies optimization opportunities, and implements control adjustments without requiring external expert intervention, allowing each building to self-optimize based on its unique characteristics.
Solution Approach 2:
The system dynamically adjusts operational parameters such as temperature setpoints, ventilation rates, and lighting schedules based on real-time sensor data and historical patterns. By continuously modifying these parameters according to actual building conditions and performance feedback, the system objectively determines and achieves optimal energy-saving potential for each unique building.
3Adaptability or versatility
If buildings operate independently without sharing data, then each building's unique conditions are maintained, but opportunities to learn from and replicate successful energy-saving practices across buildings are lost
Solution Approach 1:
The patent adds a network dimension to traditional building management, transforming single-building operations into multi-building coordinated control. By creating a hierarchical structure with local building controllers and a central control unit that aggregates data across the network, the system enables information exchange and strategy sharing while maintaining adaptability to individual building characteristics.
Data Source
AI summary
A unit includes a receiving member of sensor information of the unit, a communication member with the other units, a storage member for storing target building information and information on sensors and facilities in an information-exchangeable manner, an identification member for identifying an internal unit and an external unit of the other building by specific information, an optimization member for reading an energy calculation expression, etc., of the internal unit, selecting an operation condition provided, and performing simulation of the entire target building, a related-unit searching member for searching the other internal unit and the external unit for a related unit, an optimization member for reading an operation condition from the related units, performing simulation for optimization, and an operation member for operating the facilities on the basis of the operation condition and the sensor information.