Meter Data Cloud Intermediary for Secure Energy Analytics
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Solution Overview
Problem
The collection, storage, and management of meter data in the power industry are hindered by the large volume of data points from multiple meters, security concerns, and network restrictions, which prevent seamless access and analysis, especially when traversing public networks like the Internet, where data can be intercepted or altered by malicious hosts.
Innovation Solution
Implementing a Meter Data Cloud for centralized storage and management, allowing meters to push data outbound through firewalls using secure protocols, and using an intermediary data collector to facilitate data upload, enabling meters to register themselves and use arbitrary local Internet addresses, and integrating a machine learning system for predictive analytics and energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If meter data is stored in a central data server accessible from anywhere in the world, then data access and management is simplified, but security risks increase due to public Internet accessibility
Solution Approach 1:
The patent introduces a cloud server as an intermediary between meters and users. The cloud server receives data from meters via secure protocols, stores it centrally, and provides controlled access to authorized users through web browsers. This intermediary architecture enables global data access while maintaining security through centralized control and authentication mechanisms.
2Adaptability or versatility
If meters push data outbound through firewalls using secure protocols, then network restrictions are overcome, but data transmission complexity increases
Solution Approach 1:
The patent implements a universal data transmission mechanism where meters use standardized secure protocols (HTTPS, VPN) that are widely supported across different network environments. The cloud server provides a single endpoint that works through various firewall configurations, making the system adaptable to diverse network restrictions without requiring complex custom solutions for each scenario.
3Productivity
If an intermediary data collector is provided within the private network, then data collection is facilitated, but network configuration complexity increases
Solution Approach 1:
The patent implements a self-service data collection mechanism where the intermediary data collector automatically discovers meters on the private network, establishes secure connections, and initiates data uploads without manual configuration. The system performs self-registration and automatic authentication, eliminating the need for complex manual network configuration while maintaining high data collection efficiency.
4Reliability
If meters use arbitrary local only Internet addresses, then network security is enhanced, but data routing complexity increases
Solution Approach 1:
The patent uses the cloud server as a mediator that handles all external communications. Meters with local-only addresses communicate exclusively with the cloud server through established secure channels. The cloud server manages address translation and routing, allowing meters to maintain secure local addresses while enabling external access through the intermediary without requiring meters to handle complex routing logic.
Data Source
AI summary
Devices, systems and methods are provided for comparing energy-related data among a number of facilities or building associated with a single enterprise and reporting energy calculations to an enterprise manager. A method, according to one implementation, includes the step of receiving parameters related to the consumption of energy at a plurality of facilities of an enterprise. Based on the received parameters, the method further includes the step of calculating a grading index for each facility. The method also includes ranking the facilities based on the calculated grading indices and predicting a positive expected result in response to improving one or more lower-ranked facilities to match the grading index of a higher-ranked facility. The calculating the grading index may include calculating an energy efficiency value for each facility and/or calculating a risk factor for each facility, the risk factor related to power quality issues.