Room temperature estimating device, program
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Solution Overview
Problem
Existing methods for estimating room temperature in buildings are complex and require extensive information, making them inconvenient for practical use, especially as they struggle to accurately account for various factors influencing room temperature without relying on complicated computer simulations.
Innovation Solution
A room temperature estimating device that uses simple linear regression analysis based on outside air temperature data to produce prediction formulas for different times of day, incorporating correction information for factors like sunlight, ventilation, and occupancy, allowing for accurate room temperature estimation without extensive data input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer simulation is used to estimate room temperature based on multiple factors (outside air temperature, heat insulating property, sunlight, ventilation, etc.), then estimation accuracy is improved, but device complexity and data input requirements increase significantly
Solution Approach 1:
The patent extracts only the essential factor (outside air temperature) from the multiple factors that influence room temperature, eliminating the need to measure or input complex parameters like heat insulating property, sunlight, and ventilation. This extraction principle resolves the contradiction by maintaining reasonable estimation accuracy while dramatically simplifying the system.
Solution Approach 2:
The patent creates a simplified predictive model that copies the essential relationship between outside air temperature and room temperature based on historical data, rather than implementing a full physical simulation. This allows the system to estimate room temperature using a simple formula derived from past observations, avoiding complex simulations while maintaining practical accuracy.
2Measurement precision
If computer simulation with multiple factors is used to estimate room temperature, then estimation accuracy is improved, but ease of operation deteriorates due to extensive data input requirements
Solution Approach 1:
The patent removes the burden of inputting multiple complex parameters by extracting only the outside air temperature as the required input. Users no longer need to provide data about heat insulating property, sunlight conditions, ventilation rates, or occupancy, making the system as convenient as checking the weather forecast while still providing useful room temperature estimates.
Solution Approach 2:
The system automatically learns the relationship between outside air temperature and room temperature from historical data and performs self-calibration, eliminating the need for users to manually input building-specific parameters or conduct measurements. The system serves itself by automatically adapting to the specific building's characteristics over time.
3Ease of operation
If history data of room temperature is used to estimate future room temperature, then ease of operation is improved, but estimation accuracy deteriorates when outside air temperature changes significantly
Solution Approach 1:
The patent transitions from a static historical average approach to a dynamic predictive model that adapts to changing conditions. The system continuously learns the relationship between outside air temperature and room temperature, allowing it to dynamically adjust predictions when outdoor conditions change, thereby maintaining accuracy while preserving simplicity.
Solution Approach 2:
The system incorporates feedback mechanisms where actual room temperature measurements are used to refine and update the predictive model over time. This feedback loop allows the system to learn from past performance and improve its predictions, maintaining accuracy even as external conditions change while keeping the user interface simple.
Data Source
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AI summary
A room temperature estimating device (10) includes a storage (13), a prediction formula producer (15), a prediction change obtainer (16), and a room temperature estimator (17). The prediction formula producer (15) produces, based on pieces of room temperature data and pieces of outside air temperature data corresponding to a specified time in each of two or more days during a given extraction period, which are stored in the storage (13), a prediction formulae expressing a relation between the pieces of the room temperature data and the pieces of the outside air temperature data at the specified time. The room temperature estimator (17) determines, based on a temporal change in outside air temperature obtained by the prediction change obtainer (16), an outside air temperature at a target date and time corresponding to the specified time, and applies the determined outside air temperature to the prediction formula, and thereby to estimate a room temperature at the target date and time.