Ensemble Information Broker Architecture for Energy Data Coordination
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Solution Overview
Problem
Current intelligent control systems face challenges in effectively managing energy and data resources across multiple devices while optimizing energy cost and user productivity, particularly in smart systems, as they often focus on individual device control rather than coordinated resource sharing and coordination among devices.
Innovation Solution
A distributed data and energy storage internet architecture utilizing Ensemble Information Brokers (EIBs) that collect and analyze device data, human presence, and activity data to predict future energy and data requirements, generating control commands based on a dynamic human-centric cost function, and establishing peer-to-peer connections for resource trading using point-based credit schemes and friend connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If intelligent control systems focus on individual device control, then device control precision is improved, but resource sharing and coordination among devices deteriorates
Solution Approach 1:
The patent merges individual device control functions with network-level coordination through the Energy Broker architecture. Multiple Energy Brokers at different hierarchical levels (building, campus, utility) work together to simultaneously achieve precise device control and coordinated resource sharing across the entire network, resolving the contradiction between individual precision and collective adaptability.
Solution Approach 2:
The control system is segmented into hierarchical levels with Energy Brokers operating at building, campus, and utility levels. Each broker maintains precise control for its local devices while higher-level brokers coordinate resource sharing across multiple buildings or campuses, enabling both precise local control and broad resource coordination simultaneously.
2Productivity
If distributed Energy Brokers communicate and share data, then resource sharing efficiency is improved, but network complexity and communication overhead increases
Solution Approach 1:
The network is segmented into hierarchical Energy Broker levels that process and filter information locally before communicating upward. This segmentation reduces communication overhead by preventing all brokers from directly interacting with each other, thus improving resource sharing efficiency while managing network complexity through structured hierarchical communication.
Solution Approach 2:
Higher-level Energy Brokers act as intermediaries that aggregate and process data from multiple lower-level brokers before making coordination decisions. This intermediary layer simplifies communication by reducing the number of direct peer-to-peer interactions needed, thereby improving resource sharing efficiency without proportionally increasing network complexity.
3Measurement precision
If real-time data collection and analysis is performed across all devices, then energy optimization accuracy is improved, but computational load and data processing time increases
Solution Approach 1:
Data collection and analysis are segmented and distributed across multiple Energy Brokers at different hierarchical levels. Each broker processes data for its local devices, performing preliminary analysis and filtering before passing aggregated information upward. This segmentation maintains high energy optimization accuracy through comprehensive data collection while reducing overall processing time by enabling parallel processing across the distributed network.
Solution Approach 2:
Lower-level Energy Brokers perform preliminary data processing, filtering, and aggregation before passing information to higher-level brokers. This preliminary action reduces the volume and complexity of data that requires intensive processing at upper levels, thereby maintaining energy optimization accuracy while significantly reducing total data processing time across the system.
Data Source
AI summary
A method implemented in a network element (NE) configured to operate as an Ensemble Information Broker (EIB) within a distributed data and energy storage internet architecture comprising collecting energy data indicating a flow of energy, an amount of energy consumed and generated by devices; collecting human presence data; collecting human activity data; predicting future energy consumption requirements and generation by employing prediction algorithms and analyzing the collected data; generating a set of control commands based on the predicted future energy consumption requirements and energy generation as applied to a cost function; transmitting the set of control commands to the corresponding devices; transmitting a broadcast message to determine an external NE to establish as a friend connection based on a user preference; transmitting a request to establish a friend connection with the determined NE; and transmitting the human presence data to the external NE when the friend connection is established.