Lighting System Usage Modeling for Energy Budget Control
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Solution Overview
Problem
Current lighting systems lack efficient management and resource allocation, leading to excessive energy consumption and costs, as they often rely on manual operation and lack intelligent control mechanisms to optimize power and illumination usage.
Innovation Solution
A lighting system managed by a model that specifies rules constraining the consumption of units such as power and illumination, allowing for dynamic adjustments and actions like device shutdown or dimming to stay within allocated budgets, enabling a 'Lighting as a Service' (LaaS) model where entities pay for a units budget and are charged extra for exceeding it.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If manual operation and traditional lighting systems are used, then device complexity is low, but energy consumption and costs increase due to lack of intelligent control
Solution Approach 1:
The lighting system automatically monitors its own usage patterns, applies learned models to predict future consumption, and self-adjusts operations without manual intervention. The system serves itself by optimizing energy consumption based on historical data and learned behaviors, eliminating the need for complex manual control while reducing energy waste.
Solution Approach 2:
The system continuously collects feedback from usage data, applies this feedback to refine its predictive models, and adjusts lighting operations accordingly. This closed-loop feedback mechanism enables the system to learn from past consumption patterns and optimize future energy usage, resolving the contradiction between simplicity and energy efficiency.
2Productivity
If lighting systems operate without usage modeling, then system simplicity is maintained, but resource allocation efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal lighting configurations based on historical usage patterns and predictive models. Before actual operation, the system determines the most efficient resource allocation strategy, allowing for automated decision-making that improves productivity without requiring complex real-time management interventions.
Solution Approach 2:
The system dynamically adjusts lighting operations based on changing usage patterns and conditions. By making the system adaptive and flexible rather than static, it can optimize resource allocation in real-time without requiring complex manual management, thus improving productivity while keeping operational complexity manageable through automation.
3Loss of energy
If no usage constraints are imposed, then operational flexibility is high, but energy costs and waste increase
Solution Approach 1:
The system changes operational parameters such as lighting intensity, duration, and scheduling based on learned usage patterns and constraints. By dynamically adjusting these parameters rather than maintaining fixed operations, the system reduces energy waste while preserving operational flexibility through data-driven optimization rather than rigid constraints.
Solution Approach 2:
The system implements periodic actions by scheduling lighting operations based on historical usage patterns and seasonal variations. Rather than continuous operation, the system activates lighting during optimal periods and maintains constraints during low-demand periods, reducing energy waste while preserving flexibility through pattern-based scheduling rather than arbitrary limitations.
Data Source
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AI summary
Techniques are described for managing a lighting system that includes light sources (e.g., fixtures and/or bulbs), controller(s), sensor(s), and/or other device(s). The device(s) can be monitored to determine a units metric describing the operation of the device. A units metric can indicate a current number of units for the device, where a unit is a unit of power consumed by the device, a unit of illumination provided by the device, and/or some other unit that measures the current operating state of the device. A model can be applied to control usage of the lighting system. For example, a model may provide a budget of units that the lighting system is allowed to consume at any given time. The model may also include a set of rules that constrain the operation of the lighting system, and/or that describe actions to be taken if the budget of units is exceeded.