Climate-Control Power-Demand Identification From Runtime Data
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Solution Overview
Problem
Current methods for determining power-draw in climate-control systems are inefficient, as they often require manual data retrieval and fail to distinguish between different energy usage patterns, leading to suboptimal energy management in commercial or residential buildings.
Innovation Solution
A computer-implemented method that correlates energy usage data with run-time data using regression analyses and finite mixture models to accurately determine power-draw, enabling remote monitoring and continuous assessment of climate-control systems without prior knowledge of the system's specifics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data retrieval methods are used to determine power-draw, then system complexity is reduced, but measurement precision and productivity deteriorate due to inefficiency and inability to distinguish different energy usage patterns
Solution Approach 1:
The system automatically retrieves and processes energy usage data and run-time data without manual intervention. The computer system performs self-service by autonomously correlating data, performing regression analyses, and generating power-draw determinations, thereby improving measurement precision while managing complexity through automation.
Solution Approach 2:
Manual data retrieval and analysis methods are replaced with automated computer-implemented data processing. The mechanical/manual process of data collection and analysis is substituted with electronic data retrieval, automated correlation algorithms, and computational regression analyses, improving both precision and efficiency.
2Productivity
If automated data correlation and regression analysis are implemented, then productivity and measurement precision improve, but device complexity increases due to multiple analysis methods and data processing requirements
Solution Approach 1:
The data processing system is segmented into distinct functional components: data retrieval modules that collect energy usage data and run-time data separately, correlation modules that associate the two data types, and regression analysis modules that perform specific statistical analyses. This segmentation improves productivity by enabling parallel processing while managing complexity through modular design.
Solution Approach 2:
The computer system performs multiple functions within a unified framework: retrieving energy usage data, retrieving run-time data, correlating the data, performing various regression analyses (ordinary least squares, weighted least squares, robust regression), and generating power-draw determinations. This multi-functionality improves productivity by consolidating multiple operations into a single integrated system.
3Loss of time
If continuous monitoring and analysis are performed, then loss of time is reduced and measurement precision improves, but use of energy increases due to continuous data processing
Solution Approach 1:
The system performs data correlation and regression analysis at periodic intervals rather than continuously. Energy usage data and run-time data are collected continuously, but the computationally intensive correlation and analysis operations are performed periodically, thereby reducing energy consumption for processing while maintaining timely power-draw assessments.
Solution Approach 2:
Energy usage data and run-time data are pre-collected and stored before analysis is performed. This preliminary data collection allows the system to batch process information efficiently, reducing the time required for actual analysis and minimizing the energy required for continuous real-time processing.
Data Source
AI summary
Methods and computer systems are disclosed for identifying the power-demand of a climate-control system at a premises to reduce usage of energy at the premises. A computer system receives energy usage data and run-time data of the climate-control system. The operational data includes an on-time and/or off-time associated with the climate-control system. The computer system determines a power-draw of the climate-control system using the energy usage data and the run-time data and outputs the power-draw to be used to reduce the energy usage at the premises.