Optimization of energy use through model-based simulations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing HVAC systems require manual configuration and adjustment to adapt to changing energy needs and costs, failing to account for energy usage and costs effectively, leading to suboptimal energy consumption and production.
Innovation Solution
A system that uses model-based simulations to identify optimal energy schedules for HVAC systems by assessing components, environment, and user preferences, generating schedules that minimize energy consumption or costs based on user goals and utility demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If automated schedules are used to control HVAC systems, then energy consumption can be reduced, but the system cannot adapt to changing user needs and energy costs
Solution Approach 1:
The patent implements dynamic scheduling that automatically adjusts HVAC operation based on real-time energy costs, user availability, and comfort preferences. The system transitions from static predetermined schedules to dynamic adaptive schedules that respond to changing conditions, resolving the contradiction between energy reduction and adaptability.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor energy costs, user preferences, and system performance. This feedback enables the automated schedule to learn and adapt to changing conditions while maintaining energy efficiency, addressing both the energy conservation goal and the adaptability requirement.
2Ease of operation
If manual configuration is required for HVAC schedules, then user control is maintained, but energy optimization is suboptimal due to user inability to account for all factors
Solution Approach 1:
The patent introduces an intelligent intermediary system that acts as a mediator between user preferences and HVAC control. This intermediary automatically processes complex energy cost data, weather information, and user preferences to generate optimized schedules, maintaining user control through preference specification while achieving superior energy optimization that users cannot accomplish manually.
Solution Approach 2:
The system enables self-service optimization by automatically adjusting HVAC schedules based on energy costs and user availability without requiring continuous manual intervention. Users define their comfort preferences once, and the system autonomously optimizes energy usage, combining ease of operation with high optimization efficiency.
3Device complexity
If predetermined temperature schedules are used, then system operation is simplified, but energy costs are not minimized due to lack of real-time optimization
Solution Approach 1:
The system performs preliminary actions by pre-cooling or pre-heating spaces before peak energy cost periods based on forecasted energy prices and user schedules. This anticipatory approach simplifies operation while minimizing energy costs by shifting load to lower-cost periods, achieving both simplicity and cost optimization.
Solution Approach 2:
The patent dynamically changes operational parameters such as temperature setpoints, equipment runtime, and load distribution based on real-time energy costs and conditions. This parameter optimization maintains operational simplicity through automated adjustment while achieving significant energy cost reductions compared to fixed schedules.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A facility implementing systems and/or methods for achieving energy consumption/production and cost goals is described. The facility identifies various components of an energy system and assesses the environment in which those components operate. Based on the identified components and assessments, the facility generates a model to simulate different series/schedules of adjustments to the system and how those adjustments will effect energy consumption or production. Using the model, and based on identified patterns, preferences, and forecasted weather conditions, the facility can identify an optimal series or schedule of adjustments to achieve the user's goals and provide the schedule to the system for implementation. The model may be constructed using a time-series of energy consumption and thermostat states to estimate parameters and algorithms of the system. Using the model, the facility can simulate the behavior of the system and, by changing simulated inputs and measuring simulated output, optimize use of the system.