Distributed Forecast Devices for Utility Data Processing
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Solution Overview
Problem
Centralized utility management systems face challenges with increased data volume and frequency from automatic interval meter readings, requiring more efficient energy forecasting and resource management.
Innovation Solution
A distributed utility management system with remote forecast devices that collect and process interval meter data, using weather and account information to generate accurate energy usage forecasts, and communicate securely with a central server for planning purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If centralized architecture is used for energy profiling and forecasting, then system management is simplified, but the system cannot handle increased data volume and frequency from automatic interval readings
Solution Approach 1:
The patent divides the centralized forecasting system into distributed forecast devices located at various utility locations. Each device independently processes energy usage data for its local region, segmenting the data processing workload and enabling the system to handle increased data volumes from automatic interval readings without overwhelming a single central system.
Solution Approach 2:
The patent transitions from a single-dimensional centralized processing model to a multi-dimensional distributed architecture. Forecast devices are deployed across multiple geographic and functional dimensions within the utility network, allowing parallel processing of data streams and enabling the system to accommodate higher data frequencies and volumes through spatial distribution.
2Loss of information
If automatic interval readings are implemented, then data availability increases, but the burden on central servers increases
Solution Approach 1:
The patent extracts the data processing function from the central server and relocates it to distributed forecast devices at the edge of the network. This extraction allows automatic interval readings to be collected and processed locally, maintaining high data availability while preventing the central server from becoming a processing bottleneck.
Solution Approach 2:
The patent introduces distributed forecast devices as intermediary components between the automatic reading collection infrastructure and the central server. These intermediaries perform preliminary data processing and forecasting locally, reducing the data burden transmitted to and processed by central servers while maintaining system-wide data availability.
3Measurement precision
If more frequent readings are collected, then forecasting accuracy improves, but processing requirements increase
Solution Approach 1:
The patent applies local quality by deploying forecast devices with appropriate processing capabilities at specific utility locations where data is generated. Each device processes data with high precision for its local context, enabling accurate forecasting from frequent readings without requiring all processing power to be concentrated in one location, thus distributing the processing load efficiently.
Data Source
AI summary
Systems, apparatus, and methods are disclosed for managing and forecasting energy usage. A distributed forecast device is located remote from a central server. The distributed forecast device receives from the central server information related to one or more accounts associated with the distributed forecast device. The distributed forecast device receives energy usage data from one or more energy meters for each of the accounts. The distributed forecast device predicts an energy usage forecast for each of the accounts based on the energy usage data and the information from the central server.