Thermostat set point identification
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Solution Overview
Problem
Current methods for identifying and managing thermostat set points are inefficient, leading to increased energy consumption in heating and cooling systems, as they fail to accurately predict energy usage and do not provide effective tools for users to optimize their settings.
Innovation Solution
A system that estimates thermostat set points by selecting candidate points, calculating predicted energy usage, comparing with actual usage to determine error values, and applying penalty functions to identify the most accurate set points, thereby providing users with insights to manage their energy usage more efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional thermostat set point identification methods are used, then the system is simple to operate, but energy consumption is high due to inaccurate set point estimation
Solution Approach 1:
The system performs preliminary analysis of energy usage patterns and environmental data before determining optimal thermostat set points. By pre-processing historical data and identifying usage patterns in advance, the system can make accurate set point recommendations without requiring complex real-time calculations, thus reducing energy consumption while maintaining high estimation accuracy.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual energy consumption and comparing it with predicted consumption based on estimated set points. This feedback loop allows the system to refine its estimation algorithms over time, improving accuracy while maintaining energy efficiency through iterative optimization rather than requiring increasingly complex computational methods.
2Measurement precision
If advanced energy analysis methods are implemented, then energy usage accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the energy analysis process into distinct functional modules: data collection, pattern recognition, set point estimation, and recommendation generation. Each module handles a specific aspect of the analysis, allowing the system to achieve high prediction accuracy through specialized processing while keeping overall system complexity manageable through modular architecture and clear separation of concerns.
3Productivity
If detailed energy usage monitoring is implemented, then energy efficiency optimization is improved, but loss of information increases due to data processing requirements
Solution Approach 1:
The system extracts only the most relevant features and patterns from the collected energy usage data, such as occupancy patterns, temperature preferences, and usage timing. By selectively extracting key information rather than processing all raw data, the system achieves effective energy efficiency optimization while minimizing data processing overhead and information loss.
Data Source
AI summary
A thermostat set point estimation method and system that selects a plurality of candidate thermostat set points, determines for each of the plurality of candidate thermostat set points a predicted energy usage amount corresponding to the candidate thermostat set point, determines for each of the plurality of candidate thermostat set points an error value corresponding to the candidate thermostat set point using an actual energy usage amount and the predicted energy usage amount corresponding to the candidate thermostat set point, and identifies an estimated thermostat set point by selecting the candidate thermostat set point having the error value that is lowest from the plurality of candidate thermostat set points.