Load Power Device for Plug-in Load Identification and Energy Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current power consumption monitoring and energy management systems for plug-in electric loads (PELs) lack effective and non-intrusive methods to identify operating modes and communicate energy consumption to building management systems, often misclassifying devices in low-power modes, leading to inefficient energy savings.
Innovation Solution
A load power device with sensors to detect voltage and current, coupled with a processor for load identification and management, enabling real-time energy monitoring and control, including a smart receptacle system that distinguishes between always-on and controllable loads, and employs AI for occupancy estimation and customized management policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic power quality signatures are used to identify load operating status, then energy consumption monitoring is achieved, but misclassification of devices in low-power modes occurs
Solution Approach 1:
The system transitions from using basic power quality signatures to analyzing wavelet coefficients and spectral content of current and voltage waveforms. This parameter transformation enables more precise differentiation between standby mode and ON-OFF behavior by examining frequency domain characteristics and time-frequency representations of the electrical signals.
Solution Approach 2:
The system implements a feedback mechanism where the processor continuously monitors outlet electric signals, compares them against learned load profiles, and adjusts classification decisions based on patterns recognized from multiple measurement cycles. This allows the system to distinguish between genuine standby states and temporary power interruptions in ON-OFF devices.
2Loss of energy
If automatic load control is implemented based on power consumption thresholds, then energy savings are achieved, but user acceptance decreases due to improper OFF cycles
Solution Approach 1:
The control system dynamically adapts its behavior based on identified load characteristics. For loads with ON-OFF behavior patterns, the system learns the normal cycling behavior and avoids triggering false standby detections. For loads with true standby modes, the system applies threshold-based control. This dynamic adaptation maintains user acceptance while achieving energy savings.
Solution Approach 2:
The system performs self-learning of load profiles by monitoring electrical characteristics over time. Each load's unique signature is captured and stored, enabling the system to automatically distinguish between different device types and their operational patterns without user intervention or manual configuration.
3Measurement precision
If detailed load analysis is performed to improve classification accuracy, then operating mode identification improves, but system complexity increases
Solution Approach 1:
The signal processing is segmented into distinct analytical stages: wavelet transform for time-frequency analysis, spectral content extraction for frequency domain characterization, and pattern recognition for classification. This segmentation allows complex analysis to be performed in manageable steps, reducing computational burden while maintaining high classification accuracy.
Data Source
AI summary
A load power device includes a power input, at least one power output for at least one load, a plurality of sensors structured to sense voltage and current at the at least one power output, and a processor. The processor provides: (a) load identification based upon the sensed voltage and current, and (b) load control and management based upon the load identification.