Estimation method, simulation method, estimation device, and estimation program
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Solution Overview
Problem
In predicting indoor temperature changes, existing methods face challenges due to the need for large amounts of past data and the difficulty in measuring and setting appropriate boundary conditions, especially in dynamic environments like office buildings where air conditioner specifications and external factors like people flow affect temperature.
Innovation Solution
An estimation method that sets boundary conditions based on actual measured values and parameters with weights, calculates predicted values through simulation, and iteratively adjusts parameters to reduce errors, allowing for accurate estimation of boundary conditions without relying solely on past data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning method is used to predict indoor temperature changes, then prediction capability is improved, but a large amount of past data is required which is difficult to obtain in normal operation
Solution Approach 1:
The patent merges machine learning methods with CFD simulation techniques to create a hybrid prediction system. The machine learning model processes readily available operational data while the CFD simulation generates synthetic temperature change patterns, combining both approaches to achieve reliable predictions without requiring large amounts of historical data
Solution Approach 2:
The system performs preliminary CFD simulations to pre-calculate temperature change patterns under various boundary conditions. These pre-calculated results are then used by the machine learning model to make predictions, eliminating the need to collect extensive historical data during normal operation
2Reliability
If CFD simulation is used to predict temperature changes, then dependency on past data is reduced, but appropriate boundary conditions are difficult to set due to unmeasurable factors
Solution Approach 1:
The patent applies partial action by focusing CFD simulations only on the most critical boundary conditions that have the greatest impact on temperature changes. Rather than attempting to model all possible boundary conditions, the system identifies and simulates only the key factors, reducing complexity while maintaining prediction accuracy
Solution Approach 2:
The system changes parameters by using standardized boundary condition values from air conditioner specifications and catalogs as defaults. These standard parameters are then adjusted based on available measurement data, simplifying the boundary condition setting process while maintaining simulation accuracy
3Stability of the object's composition
If air conditioner parameters are kept constant to maintain comfort, then operational stability is improved, but only biased data is acquired making it difficult to learn air conditioner influence
Solution Approach 1:
The patent introduces dynamics by creating a virtual testing environment where air conditioner parameters can be changed without affecting actual indoor comfort. The CFD simulation allows dynamic adjustment of parameters such as air volume, temperature, and flow patterns to generate diverse training data, while the real system maintains stable operational conditions
4Measurement precision
If sensors are attached to blowoff ports to measure air volume, then measurement accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of the air conditioner system through CFD simulation. Instead of physically measuring air volume at blowoff ports with sensors, the system creates a digital replica that calculates air flow characteristics based on boundary conditions, providing measurement data without physical sensor installation
Solution Approach 2:
The patent replaces the mechanical sensor-based measurement system with a computational approach. CFD simulation substitutes physical sensors by using numerical methods to calculate air volume and flow characteristics, eliminating the need for complex sensor installation while providing accurate measurement data
Data Source
AI summary
An estimation device 10 estimates a boundary condition used in a simulation of a temperature inside a target space. A boundary condition setting unit 109 of the estimation device 10 sets the boundary condition based on an actual measured value of observation data related to the target space and a parameter including a weight to the actual measured value of the observation data. Then, a simulation execution unit 110 calculates a predicted value of the observation data by executing a simulation inside the target space based on the boundary condition set. Then, an error calculation unit 112 calculates an error between the predicted value of the observation data calculated through the simulation and the actual measured value of the observation data. Then, a parameter update unit 113 estimates the parameter so as to reduce the error. Then, the parameter update unit 113 estimates the boundary condition based on the parameter estimated.