HVAC Energy Scheduling Using Model-Based Simulation
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Solution Overview
Problem
Existing energy management systems fail to adapt to changing energy costs and user needs, as they do not consider the amount of energy used or its cost, requiring users to manually adjust settings which is inefficient and not responsive to volatile energy markets.
Innovation Solution
A facility that uses model-based simulations to identify optimal energy usage schedules based on user preferences and environmental factors, such as weather and energy pricing, by assessing energy systems and environments, and employing optimization techniques to minimize energy consumption or costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated schedules are used to control energy systems, then ease of operation is improved, but adaptability to changing energy costs and user needs deteriorates
Solution Approach 1:
The system continuously monitors energy consumption data, user behavior patterns, and external factors (weather, energy costs) to dynamically adjust schedules. This feedback loop enables the system to adapt to changing conditions while maintaining automated operation, resolving the contradiction between ease of operation and adaptability.
Solution Approach 2:
The energy management system transitions from static predetermined schedules to dynamic adaptive schedules that automatically adjust based on real-time data. The system modifies temperature setpoints, timing, and energy consumption patterns dynamically, enabling both automated operation and adaptability to changing conditions.
2Use of energy by moving object
If model-based simulations are implemented to optimize energy usage, then energy consumption is reduced, but device complexity increases
Solution Approach 1:
The system creates virtual copies (simulation models) of the physical energy systems to test and optimize schedules without affecting actual energy consumption. These digital twins allow comprehensive optimization analysis while the physical systems continue operating with minimal additional complexity.
Solution Approach 2:
The system performs simulations and optimizations in advance to determine optimal schedules before implementing them in the actual energy systems. This preliminary action allows comprehensive optimization to be done offline, reducing the computational complexity required during real-time operation.
Data Source
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.