Development of certain building data profiles and their use in an automated optimization method to reduce thermal energy consumption in commercial buildings during the heating season
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Solution Overview
Problem
Commercial buildings often experience inefficiencies in energy usage due to oversized heating and cooling plants, excessive operation times, and mismatched control strategies, leading to overheating and over-cooling, which result in high energy consumption and occupant discomfort.
Innovation Solution
The method involves deriving unique thermal parameters such as natural thermal lag, mechanical heating rate, and natural cooling rate from readily available data to optimize the operation of heating and cooling systems, allowing for improved regulation and reduced energy consumption by determining optimal start times for heating and cooling based on external temperature forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If heating and cooling plants are oversized to ensure comfort during extreme weather, then occupant comfort is improved, but energy consumption increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-heating or pre-cooling buildings during off-peak hours when external temperatures are favorable, storing thermal energy in the building's thermal mass. This allows the plants to operate at reduced capacity during peak demand periods while maintaining comfort, thereby reducing overall energy consumption.
Solution Approach 2:
The control system dynamically adjusts plant operation based on real-time weather forecasts, building occupancy patterns, and thermal lag characteristics. This dynamic optimization ensures plants operate only when necessary and at optimal capacity, preventing energy waste from oversized plants running continuously at partial load.
2Reliability
If heating systems operate for longer periods to maintain comfortable temperatures, then occupant comfort is improved, but energy consumption and operational costs increase
Solution Approach 1:
The system performs preliminary heating or cooling actions during off-peak hours when external temperatures are favorable, storing thermal energy in the building's thermal mass. This allows the plants to operate at reduced capacity during peak demand periods while maintaining comfort, thereby reducing overall energy consumption.
Solution Approach 2:
The system continuously monitors internal and external temperatures, occupancy patterns, and plant performance, using this feedback to optimize operation schedules. This ensures heating operates only when and where needed, reducing unnecessary operation time while maintaining comfort through adaptive control.
3Ease of operation
If simple control strategies are used for heating and cooling plants, then ease of operation is improved, but thermal efficiency decreases leading to energy waste
Solution Approach 1:
The system automatically determines optimal operation schedules by analyzing weather forecasts, building thermal characteristics, and occupancy patterns without requiring manual intervention. This self-service approach maintains simplicity for users while achieving advanced thermal optimization that reduces energy waste.
Solution Approach 2:
The system changes operational parameters such as start/stop times, temperature setpoints, and plant capacity based on real-time conditions and predictions. This dynamic parameter adjustment optimizes thermal efficiency while maintaining ease of operation through automated control.
4Measurement precision
If detailed thermal simulation tools are used to accurately predict building performance, then measurement precision is improved, but device complexity and implementation cost increase
Solution Approach 1:
The system extracts only the essential thermal characteristics needed for optimization, such as thermal lag and heat capacity, from detailed building models. This simplified approach captures the dominant thermal behaviors without requiring complex simulation tools, reducing implementation complexity while maintaining sufficient accuracy for control optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces thermal energy usage in commercial buildings by optimizing plant operations, achieving energy savings of up to 54% while maintaining occupant comfort, as demonstrated in a test building implementation.
Implementation Method 1
where buildings are over-cooled in summer, buildings are very effective in absorbing heat from the external environment to compensate
Implementation Method 2
The underlying principles of these tools concentrate on thermal properties of individual elements of the building itself
Implementation Method 3
Where the common problem of overheating occurs, the building envelope is quite efficient in dumping excess heat by radiation
Data Source
AI summary
The invention teaches a system and method for reducing energy consumption in commercial buildings. The invention provides development of certain mechanical heat profiles and use of such profiles in an automated optimization method. Outputs communicate with the building management system of the commercial building, and regulate the heating system during a season when the building activates the heating system. Various embodiments are taught.