Secure Energy Management System with Neural Network Prediction
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Solution Overview
Problem
Smart meters raise consumer privacy concerns due to detailed energy usage data transmission, which can reveal behavioral patterns, and existing energy management systems lack centralized control and security measures to optimize energy infrastructure effectively.
Innovation Solution
A flexible, secure energy management system that includes a system management component for centralized optimization, modular secure terminal devices for data input and control, and a system gateway for encrypted communication, utilizing a specially trained artificial neural network for dynamic prediction and adaptive energy modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If smart meters transmit detailed energy usage data, then energy consumption monitoring is improved, but consumer privacy is compromised
Solution Approach 1:
The patent extracts only the essential energy consumption data needed for billing and monitoring, while removing detailed behavioral information that would compromise privacy. The system aggregates fine-grained usage data into summarized metrics that maintain measurement precision for billing purposes but eliminate sensitive patterns about consumer behavior.
Solution Approach 2:
The patent introduces an intermediary processing layer between the smart meter and the utility system that anonymizes and aggregates data. This intermediary component transforms detailed usage information into privacy-preserving summaries while maintaining the accuracy needed for energy management and billing.
2Productivity
If detailed energy usage data is stored and transmitted, then energy optimization control is improved, but data security requirements increase
Solution Approach 1:
The patent extracts and transmits only the minimum necessary data for energy optimization control, removing redundant detailed information. By sending aggregated metrics rather than complete raw data, the system maintains optimization capability while reducing the security burden of protecting extensive datasets.
Solution Approach 2:
The patent applies different data processing qualities to different purposes: detailed data is processed locally for optimization control where needed, while only summarized data is transmitted for billing and remote monitoring. This local quality approach maintains security by limiting data transmission while preserving optimization capabilities locally.
3Productivity
If extensive energy customer usage data is stored, then system optimization is improved, but storage requirements and privacy risks increase
Solution Approach 1:
The patent performs preliminary aggregation and anonymization of energy usage data at the source before transmission or storage. By preprocessing data into optimized summaries in advance, the system reduces the quantity of data that needs to be stored and transmitted while maintaining the information necessary for system optimization.
Solution Approach 2:
Instead of storing all detailed data and then processing it, the patent inverts the approach by first aggregating and summarizing data, then storing only the condensed information. This reversal of the traditional data flow minimizes storage requirements while preserving optimization capabilities.
Data Source
AI summary
Systems and techniques are disclosed for flexible, secure energy management that enable control of individual devices in a customer site's energy infrastructure, which can allow optimization (e.g., by minimizing energy cost or usage) across the infrastructure. A system management component may centralize optimization and control capabilities across the energy infrastructure by determining an energy model and directing consumption, storage, and energy draw activities for the totality of the energy infrastructure devices. Systems may include modular, secure terminal devices that can be attached to or paired with the devices in an energy infrastructure in order to provide input data such as power load metrics, as well as to control the operation of the devices. A dynamic, adaptive prediction of system parameters, such as future demand, cost, generation capacity, and other metrics, may be provided. Dynamic, adaptive prediction may be performed without detailed historical usage data. A system gateway device may centralize communications between the system devices so that energy customer privacy is maintained.