Cloud Energy Management System Balancing Heterogeneous Device Consumption
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Solution Overview
Problem
Managing energy consumption across diverse devices in a networked environment is challenging due to varying energy usage patterns and the difficulty in making consistent measurements across different manufacturers, making it hard to determine equivalent emissions measurements and reduce emissions while maintaining functionality.
Innovation Solution
A computer-implemented method using a cloud-based system with a real-time data analytics engine, ontology domain engine, and knowledge extraction engine to analyze energy consumption patterns, build sustainability profiles, and optimize energy usage through AI-based multi-factor optimization criteria, leveraging Fault Managed Power and Power over Ethernet techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If energy consumption patterns are monitored across diverse devices from different manufacturers, then energy management capability is improved, but measurement consistency and data homogeneity deteriorate
Solution Approach 1:
The patent applies homogeneity by creating a standardized data model that normalizes energy consumption data from diverse devices. The system defines common data structures and measurement protocols that transform heterogeneous device data into uniform formats, enabling consistent analysis across different manufacturers and device types while maintaining measurement precision.
Solution Approach 2:
The patent introduces an intermediary layer (the energy management system with ontology engines) that mediates between diverse device data sources and the analysis processes. This intermediary standardizes data collection, applies normalization rules, and transforms varied device metrics into comparable formats, resolving the measurement consistency issue while preserving energy management capabilities.
2Object-generated harmful factors
If sustainability profiles and optimization algorithms are implemented, then emissions reduction is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex energy management system into distinct functional modules: data collection layer, ontology domain engine, knowledge extraction engine, and optimization layer. Each module handles specific tasks independently, making the overall system more manageable despite its complexity. The segmentation allows targeted optimization of emissions reduction without requiring complete system redesign.
Solution Approach 2:
The patent implements preliminary action by pre-defining sustainability profiles, ontologies, and optimization algorithms before actual energy management operations. These pre-configured frameworks are prepared in advance and can be applied systematically to reduce emissions without requiring complex real-time decision-making, thereby reducing operational system complexity.
3Productivity
If real-time data analytics and AI-based optimization are deployed, then energy usage balancing is improved, but computational resource requirements increase
Solution Approach 1:
The patent applies partial action by implementing AI-based optimization selectively for critical energy management decisions rather than continuously processing all data in real-time. The system uses analytics and optimization algorithms only when necessary to balance energy usage, reducing computational energy consumption while maintaining effective energy management through targeted interventions.
Data Source
AI summary
Presented herein are techniques for balancing energy usage. A method can include obtaining energy consumption patterns of a plurality of devices that consume energy in an environment from a plurality of data sources. The method can further include analyzing operational patterns of the plurality of devices that are dynamically controllable and associated with the environment to generate device data. The method can further include building and maintaining multiple domain ontologies for sustainability profiles. The method can further include developing a profile for energy consumption for at least one device of the plurality of devices, wherein the profile is based on the energy consumption patterns, the device data, and at least one sustainability profile of the multiple domain ontologies.