Energy Recommendation Engine for Automated Power Event Analysis
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Solution Overview
Problem
Existing energy consumption monitoring devices generate vast data but require expertise to translate this into energy savings and equipment reliability, making it labor-intensive and difficult to scale effectively.
Innovation Solution
A system comprising a central processing unit that receives and analyzes data from energy monitoring devices, determines device performance, and communicates actions to bring devices back into acceptable operation, including sending control signals and providing recommendations for energy savings and reliability improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy analysts manually review data to spot energy savings opportunities, then energy savings can be identified, but the process becomes labor intensive and hard to scale
Solution Approach 1:
The system enables self-service by implementing automated anomaly detection algorithms that independently analyze energy consumption data without requiring manual analyst intervention. The processor automatically identifies deviations from expected patterns, generates alerts, and suggests corrective actions, allowing the system to serve itself in detecting and responding to energy inefficiencies.
Solution Approach 2:
The patent replaces the mechanical system of manual data review by energy analysts with an automated electronic processing system. The processor executes algorithms that automatically analyze energy consumption data, substitute human cognitive processes with computational operations, and generate insights through machine learning models rather than human expertise.
2Loss of information
If monitoring devices gather extensive measurement data, then comprehensive energy information is available, but expertise is required to translate data into actionable insights
Solution Approach 1:
The system introduces an intermediary processing layer between raw energy data and end-users. The processor acts as a mediator that automatically translates complex measurement data into simplified alerts, notifications, and actionable recommendations. This intermediary layer filters, analyzes, and presents information in an easily consumable format without requiring user expertise in energy systems.
Solution Approach 2:
The automated anomaly detection system performs self-service by independently interpreting energy data without requiring external expertise. The processor automatically detects patterns, identifies anomalies, and generates insights autonomously, eliminating the need for specialized knowledge to translate raw data into actionable information.
3Loss of information
If manual energy analysis is performed, then detailed energy insights can be obtained, but the process is labor intensive
Solution Approach 1:
The system replaces manual energy analysis mechanics with automated computational processes. The processor executes algorithms that continuously analyze energy consumption data, substituting human analysts' time-consuming review processes with rapid machine-based detection and interpretation, maintaining insight quality while dramatically reducing time investment.
Solution Approach 2:
The automated system enables continuous analysis of energy data without interruption or labor constraints. Unlike manual analysis that occurs periodically when analysts are available, the automated processor continuously monitors energy consumption, detects anomalies in real-time, and generates alerts without breaks, maintaining high-quality insights while eliminating time loss associated with human work cycles.
Data Source
AI summary
A combination of one or more monitoring devices and a central processor at a hosted service gathers data from a customer site, identifies energy system events of interest, and analyzes the energy system event of interest to determine and recommend or implement vendor services designed to increase energy savings and/or energy system reliability.