Predictive Building Temperature Control Using Free Cooling
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Solution Overview
Problem
Existing room temperature regulation systems in buildings are inefficient in minimizing energy costs, as they often rely on costly heating and cooling sources and are prone to increased energy demands due to erroneous forecasts or model errors, leading to potential comfort losses.
Innovation Solution
A predictive control system with a hierarchical structure that optimizes the use of cost-effective energy sources like solar protection and free cooling, using a building model to generate control signals for regulating zones, incorporating PID controllers and energy recovery devices to maintain comfort within a defined temperature range, thereby reducing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If conventional PID controllers and heating/cooling water circuits are used to regulate room temperature, then the desired temperature can be maintained, but energy costs increase due to reliance on expensive heating and cooling sources
Solution Approach 1:
The system performs preliminary actions by using predictive models to forecast future temperature requirements and pre-adjusting the thermal state of the building. This allows the system to prepare for upcoming heating or cooling demands by utilizing free energy sources or thermal storage before expensive heating/cooling cycles are needed, thereby reducing energy costs while maintaining temperature comfort.
Solution Approach 2:
The system changes operational parameters by switching between different energy sources (free sources vs. expensive heating/cooling) and adjusting the comfort temperature range dynamically. By modeling the building's thermal response to parameter changes and optimizing these parameters predictively, the system reduces reliance on expensive energy sources while maintaining acceptable temperature levels.
2Use of energy by moving object
If predictive control with building models is implemented to optimize energy usage, then energy costs are reduced, but system complexity increases due to hierarchical structure and multiple control levels
Solution Approach 1:
The control system is segmented into hierarchical levels: an upper level for predictive optimization and lower levels for immediate control execution. This segmentation allows complex predictive calculations to be performed only when needed for optimization, while routine control tasks are handled by simpler lower-level controllers, thereby managing overall system complexity while achieving energy cost reduction.
Solution Approach 2:
A building model serves as an intermediary between the predictive control algorithm and the physical building system. The model translates complex thermal dynamics into simplified predictions about future temperature states, enabling the control system to make informed decisions without directly handling the full complexity of the building's thermal behavior, thus reducing control system complexity.
3Use of energy by moving object
If free energy sources and thermal storage are utilized to reduce energy costs, then reliance on expensive heating/cooling systems decreases, but the ability to maintain precise temperature control may be compromised due to forecast errors or model inaccuracies
Solution Approach 1:
The system applies beforehand cushioning by maintaining a buffer in the thermal state of the building that provides margin for error. This buffer allows the system to absorb forecast errors or model inaccuracies without compromising temperature control reliability, as the thermal mass and storage capacity provide a cushion that compensates for prediction deviations while still reducing reliance on expensive heating/cooling systems.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor actual temperature deviations from predicted values and adjust future predictions and control actions accordingly. This feedback loop compensates for forecast errors and model inaccuracies by learning from past performance, thereby maintaining temperature control reliability while continuing to utilize free energy sources effectively.
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
The system effectively reduces energy costs by efficiently utilizing thermal capacities of the building, maintaining comfort without substantial time delays, even with inaccurate forecasts, by prioritizing cost-effective energy sources and minimizing reliance on expensive heating and cooling systems.
Implementation Method 1
the system effectively reduces energy costs by efficiently utilizing thermal capacities of the building
Implementation Method 2
incorporating PID controllers and energy recovery devices to maintain comfort within a defined temperature range
Data Source
AI summary
In a building which has at least two floors, an apparatus for controlling and regulating room temperature having a hierarchical structure includes a predictive device on the top level, that provides optimal control of the use of at least one free energy source, and at least one device on a lower level which is used for feedback regulation of at least one additional energy source. The predictive device uses a building model and repeatedly optimizes the energy requirement or the energy costs. The regulation strategy of the predictive device uses characteristics of a passive heat accumulator of the building.