Smart Lighting Fixture Dimming Curve Calibration
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Solution Overview
Problem
Existing lighting fixtures, especially those with different types and models, exhibit varying power consumption patterns at different brightness levels due to manufacturing and environmental variations, leading to inefficiencies and potential malfunctions, which are difficult to detect and correct.
Innovation Solution
The development of smart lighting fixtures that perform automatic calibration to determine dimming curves and generate consumption models based on measured power consumption metrics, allowing for the identification of anomalies and remedial actions through machine learning techniques and communication with a Smart Fixture Optimization System.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If different types of lighting fixtures are deployed to provide lighting and safety, then lighting coverage and safety are improved, but power consumption varies and becomes difficult to monitor and control
Solution Approach 1:
The system implements continuous monitoring of power consumption metrics from each lighting fixture and feeds this data back to the server. The server compares actual consumption against expected consumption patterns and automatically adjusts dimming levels or sends alerts when anomalies are detected, creating a closed-loop feedback system that optimizes energy usage while maintaining lighting reliability.
Solution Approach 2:
The system enables lighting fixtures to automatically report their own power consumption status and operational health to the server. Each fixture self-monitors its metrics and transmits data without requiring manual intervention, allowing the system to autonomously identify and respond to malfunctioning fixtures through automatic dimming or alert generation.
2Measurement precision
If manual monitoring of lighting fixtures is performed, then power consumption can be detected, but it requires significant time and resources
Solution Approach 1:
Lighting fixtures automatically monitor and report their own power consumption metrics to the server without requiring manual intervention. The system self-services by continuously collecting, transmitting, and processing operational data, eliminating the need for time-consuming manual monitoring while maintaining precise measurement of power consumption across all fixtures.
Solution Approach 2:
The system implements continuous automated monitoring of power consumption metrics across all lighting fixtures. Data is collected continuously or at regular intervals and immediately transmitted to the server for analysis, replacing intermittent manual checks with uninterrupted automated surveillance that detects anomalies in real-time without time loss.
3Device complexity
If malfunctioning fixtures are not detected, then system simplicity is maintained, but power consumption increases and light output reduces
Solution Approach 1:
The system continuously monitors power consumption metrics and provides feedback to the server, which compares actual consumption against expected patterns. When deviations indicate malfunction, the system automatically responds by dimming the fixture or generating alerts, preventing energy waste from undetected failures while maintaining relatively simple operational complexity through automated rule-based responses.
Solution Approach 2:
The monitoring system enables fixtures to self-report their operational status and power consumption characteristics. The server automatically analyzes this self-reported data and triggers appropriate responses without requiring complex manual intervention or system reconfiguration, maintaining simplicity while preventing energy loss from undetected malfunctions.
Data Source
AI summary
A system described herein may provide a technique for the automated detection of a particular type of lighting fixture, where a given type may refer to a particular make and/or model of the lighting fixture, bulb quantity, bulb technology, power output rating, etc. The detection may be based on measured dimming curves of the lighting fixture, which may indicate actual power consumption at varying brightness levels of the lighting fixture. One or more power consumption models may be selected, generated, and/or refined based on the measured dimming curves, and applied to other lighting fixtures sharing similar attributes and/or under similar conditions. Based on such models, a given lighting fixture may be able to automatically detect anomalous behavior (e.g., excessive power consumption or less power consumption compared to expected power consumption) and automatically take remedial actions.