Thermostat Dispatch Engine for Weather-Based Demand Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Smart thermostats face challenges in accurately assessing occupancy and providing efficient energy usage data to utility providers for demand-response events, especially under varying weather conditions, which affects their ability to effectively reduce energy consumption during peak periods.
Innovation Solution
A thermostat management server system that collects energy usage data from multiple thermostats, simulates demand response events based on weather conditions, and selects optimal capacity reduction levels by generating statistical probabilities to communicate with utility providers for efficient energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If smart thermostats collect and process more environmental and occupancy data to improve energy management accuracy, then energy usage optimization improves, but device complexity and data processing requirements increase
Solution Approach 1:
A server acts as an intermediary between multiple thermostats and utility providers, centralizing the complex tasks of data collection, weather condition simulation, and statistical probability generation. This allows individual thermostats to remain relatively simple while the system as a whole achieves sophisticated energy management through the mediating server that processes energy usage data, simulates demand response events, and generates capacity reduction recommendations.
2Reliability
If thermostats simulate demand response events using multiple weather conditions to improve prediction accuracy, then reliability of capacity reduction commitments improves, but computational time and processing resources increase
Solution Approach 1:
The system performs preliminary simulations using historical weather conditions and occupancy patterns before actual demand response events occur. By pre-processing energy usage data and generating statistical probabilities in advance, the system reduces the computational burden during real-time events while maintaining high prediction reliability through pre-established models of thermostat behavior under various weather conditions.
Solution Approach 2:
The system creates simplified copies or models of actual demand response events through simulation, using representative weather conditions and occupancy scenarios. These simulated copies allow the system to test and validate prediction algorithms without requiring extensive real-time computational resources, thereby reducing simulation time while preserving the reliability needed for accurate capacity reduction forecasting.
3Manufacturing precision
If the system selects optimal capacity reduction levels using statistical probabilities to improve compliance accuracy, then demand-response event compliance improves, but computational complexity increases
Solution Approach 1:
The system uses feedback from simulated demand response events to continuously refine its statistical probability models. By analyzing the results of simulations and comparing predicted versus actual energy reduction outcomes, the system adjusts its algorithms to improve compliance precision over time. This feedback mechanism allows the system to achieve high accuracy in capacity reduction predictions while managing computational complexity through iterative optimization rather than requiring increasingly complex algorithms from scratch.
Data Source
AI summary
A thermostat management server may include one or more processors and one or more memory devices comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising receiving information that characterizes energy usage associated with the plurality of thermostats, receiving parameters characterizing proposed future demand-response events, selecting a combination of thermostats from the plurality of thermostats for which the energy usage can be reduced, simulating a demand response event based on the parameters and using different weather conditions for the combination of the plurality of thermostats, generating statistical probabilities of meeting a plurality of capacity reduction levels based on the different weather conditions, selecting a capacity reduction level from the plurality of capacity reduction levels based on the statistical probabilities, and sending the capacity reduction level to the utility provider computer system.