Optimization of energy use through model-based simulations
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Solution Overview
Problem
Existing energy systems lack the ability to adapt to changing energy costs and user preferences, failing to optimize energy consumption or production effectively, as they do not consider the amount of energy used or its cost.
Innovation Solution
A facility that uses model-based simulations to identify optimal schedules for energy systems like HVAC, electric vehicles, and solar systems, taking into account user preferences, energy costs, and environmental factors, to minimize energy consumption or production costs based on user goals and utility demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated schedules with predetermined times and temperatures are used, then the system can operate automatically without user intervention, but the system cannot adapt to changing energy costs and user needs
Solution Approach 1:
The patent implements dynamic schedules that automatically adjust temperature setpoints and timing based on real-time energy cost fluctuations and changing user preferences. The system transitions from static predetermined schedules to dynamic adaptive schedules that respond to external conditions, resolving the contradiction between automation and adaptability.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor energy costs, user preferences, and actual energy consumption. This feedback enables the automated system to learn from past performance and adjust future schedules optimally, maintaining automation while gaining adaptability to changing conditions.
2Adaptability or versatility
If users manually configure and adjust energy system schedules, then the system can adapt to user needs, but the system does not optimize based on energy consumption amounts or costs
Solution Approach 1:
The system performs self-optimization by automatically analyzing energy cost data, consumption patterns, and user preferences to generate optimized schedules without requiring user intervention. The system serves itself by making intelligent decisions about when and how to adjust operations, simultaneously achieving user preference adaptation and energy cost optimization.
Solution Approach 2:
The patent replaces manual user configuration and adjustment mechanisms with an intelligent automated system that uses algorithms and simulations to optimize energy usage. This substitution maintains user preference adaptation while adding the capability to optimize based on energy consumption amounts and costs.
3Loss of energy
If the system uses simulation models to optimize energy schedules, then energy consumption or costs can be minimized, but the system complexity increases
Solution Approach 1:
The patent uses simulation models that create virtual copies of the energy system to test and optimize schedules without affecting the actual system. These digital twins allow the system to evaluate multiple scenarios and identify optimal schedules while keeping the physical system simple and easy to operate.
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.