HVAC Efficiency Evaluation Using Thermal Mass and Weather Data
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Solution Overview
Problem
Conventional thermostats lack the ability to account for external weather conditions, thermal mass of buildings, and dynamic factors affecting HVAC system efficiency, leading to sub-optimal energy usage and increased energy bills.
Innovation Solution
A system that uses a single temperature sensor connected to a network, incorporating outside temperature data, thermal mass calculations, and diagnostic capabilities to optimize HVAC operation, correct for erroneous readings, and detect anomalies, thereby improving energy efficiency and user comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional thermostats are used with limited sensors, then device complexity is reduced, but measurement precision and ability to account for external conditions deteriorates
Solution Approach 1:
The system divides the thermostat functionality into two parts: a simple local thermostat device that collects basic data, and a remote server that performs complex calculations including thermal mass computation, efficiency evaluation, and optimization. This segmentation allows the local device to remain simple while achieving high measurement precision through sophisticated remote processing.
Solution Approach 2:
A network communication intermediary connects the simple thermostat to the powerful remote server, enabling the thermostat to access advanced computational resources without increasing its own complexity. The server acts as an intermediary that processes thermal mass calculations and efficiency evaluations based on data from the simple thermostat.
2Productivity
If programmable thermostats are used, then energy management capability is improved, but ease of operation deteriorates due to complex programming requirements
Solution Approach 1:
The system enables self-service energy optimization by automatically calculating thermal mass, evaluating HVAC efficiency, and generating optimized temperature schedules without requiring user programming. The server autonomously processes thermostat data and provides recommendations, eliminating the need for users to program complex schedules while maintaining high energy management efficiency.
Solution Approach 2:
The system implements continuous feedback loops where thermostat data is collected, thermal mass is calculated, HVAC efficiency is evaluated, and optimized setpoints are returned to the thermostat. This automated feedback mechanism replaces manual programming with intelligent, data-driven optimization that improves energy management without burdening users.
3Ease of operation
If thermostats are programmed infrequently, then ease of operation is maintained, but loss of time and energy savings deteriorate
Solution Approach 1:
The system maintains continuous useful action by automatically and continuously evaluating HVAC efficiency and updating optimization recommendations without requiring periodic user intervention. The server continuously processes thermostat data, calculates thermal mass effects, and provides ongoing optimization, eliminating the time loss associated with infrequent re-programming while maintaining ease of operation.
4Measurement precision
If thermostats do not account for thermal mass, then device complexity is reduced, but accuracy of temperature prediction and optimization deteriorates
Solution Approach 1:
The system adds a temporal dimension to temperature measurement by continuously collecting historical temperature data and using it to calculate thermal mass. This transforms the problem from simple instantaneous temperature reading to a time-based analysis that captures the building's thermal characteristics, improving prediction accuracy while keeping the local thermostat simple.
Solution Approach 2:
The system replaces complex local thermal mass calculation hardware with remote computational processing. Instead of requiring the thermostat to perform complex thermal calculations locally, the system substitutes this function with remote server processing that analyzes temperature data over time to derive thermal mass characteristics.
Data Source
AI summary
The invention comprises systems and methods for evaluating changes in the operational efficiency of an HVAC system over time. The climate control system obtains temperature measurements from at least a first location conditioned by the climate system, and status of said HVAC system. One or more processors receives measurements of outside temperatures from at least one source other than said HVAC system and compares said temperature measurements from said first location with expected temperature measurements. The expected temperature measurements are based at least in part upon past temperature measurements.