Providing demand response
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Solution Overview
Problem
Previous approaches to demand response have high error rates in determining baseline energy loads and fail to assess appropriate demand response programs for customers, lacking the use of thermostat data to identify energy inefficiencies and optimize energy audits.
Innovation Solution
The system receives and analyzes thermostat data and contextual information to create models for determining baseline loads, assessing suitable demand response programs, and identifying customers who benefit from energy audits, using segmentation and specific models for steady-state, setpoint-change, and transition operations of HVAC systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If consumption data from non-demand response days is used to determine baseline load, then baseline load can be calculated, but error rate in baseline determination increases
Solution Approach 1:
The patent segments thermostat data into three distinct portions: steady-state operation periods, setpoint-change operation periods, and transition periods. By creating separate models for each segment (steady-state model, setpoint-change model, and transition model), the system achieves more accurate baseline load determination that accounts for different HVAC operating conditions, thereby resolving the contradiction between calculating baseline load and maintaining high accuracy.
2Loss of information
If utilities send auditors to structures for energy audits, then energy inefficiencies can be identified, but costs increase due to increased time spent at structure
Solution Approach 1:
The patent enables self-service by using automated thermostat data analysis to identify energy inefficiencies and recommend appropriate demand response programs. The system processes HVAC operation data, creates segmented models, and automatically determines which customers would benefit most from energy audits or specific demand response programs, eliminating the need for manual auditor visits and significantly reducing audit costs while maintaining identification accuracy.
3Ease of operation
If demand response programs are offered without assessing customer appropriateness, then program provision is simplified, but program effectiveness decreases
Solution Approach 1:
The patent implements feedback by using thermostat data to continuously monitor HVAC operation patterns and customer response characteristics. The segmented models provide feedback on which demand response program types (e.g., precooling, setpoint adjustment) are most appropriate for each customer based on their specific HVAC system behavior, ensuring that recommended programs are tailored to individual customer profiles and likely to achieve effective load reduction.
Data Source
AI summary
Devices, systems, and methods for providing demand response are described herein. One device includes instructions executable to receive thermostat data associated with an operating thermostat of a heating, ventilation, and air conditioning (HVAC) system in a structure over a period of time, determine a first portion of the thermostat data corresponding to steady-state operation periods of the thermostat, determine a second portion of the thermostat data corresponding to operation periods of the thermostat that are responding to temperature setpoint changes, determine a third portion of the thermostat data corresponding to operation periods of the thermostat that are transitions between the steady-state operation periods and the operation periods that are responding to temperature setpoint changes, and create a steady-state model of the thermostat based on the first portion of the thermostat data, a setpoint-change model of the thermostat based on the second portion of the thermostat data, and a transition model of the thermostat based on the third portion of the thermostat data.