Utility Demand Footprint Visualization for Consumption Analysis
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Solution Overview
Problem
Current utility demand monitoring and billing systems lack detailed information on daily consumption patterns and weather conditions, making it difficult for utility companies to manage demand effectively.
Innovation Solution
The Utility Demand Footprint (UDF) system continuously monitors and aggregates data, presenting it visually with color mapping to characterize demand in relation to influencing factors like time of day, type of day, and weather conditions, allowing for improved management and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If utility demand data is presented in simple monthly summaries, then the billing statement remains simple and easy to understand, but detailed information about daily consumption patterns and weather conditions is lost
Solution Approach 1:
The utility demand data is segmented into multiple hierarchical levels: monthly summaries, weekly breakdowns, and daily detailed records. Each level provides appropriate detail for its purpose, allowing users to drill down from simple monthly views to detailed daily patterns when needed, thus resolving the contradiction between simplicity and information completeness.
Solution Approach 2:
The patent adds temporal dimensionality to the data presentation by organizing demand information across multiple time scales (monthly, weekly, daily, hourly) and correlating it with weather conditions. This multi-dimensional approach allows the same data to serve both simple billing purposes and detailed analytical needs without losing information at any level.
2Productivity
If detailed daily and hourly utility demand data is collected and analyzed, then utility management capability is improved, but the complexity of data processing and presentation increases
Solution Approach 1:
The data processing system is segmented into modular components that handle different time scales and data types independently. Monthly aggregation, weekly analysis, and daily monitoring are performed by separate processing modules, reducing overall system complexity while maintaining comprehensive analytical capability.
Solution Approach 2:
The system dynamically adjusts the level of data aggregation and presentation based on user needs and seasonal patterns. During peak demand periods or unusual weather conditions, the system automatically provides more detailed breakdowns, while during normal periods it presents simplified summaries, optimizing both management capability and system complexity.
3Device complexity
If monthly aggregation of utility demand data is used, then data processing is simple, but daily profiles, temperature variations, and consumption patterns are not captured
Solution Approach 1:
The aggregation process is segmented into hierarchical levels where monthly summaries are generated from weekly data, which in turn is aggregated from daily records. This segmented approach maintains simple monthly processing while preserving detailed daily patterns that can be accessed when needed, preventing information loss at any aggregation level.
Solution Approach 2:
Detailed daily and hourly data is pre-processed and stored in structured formats before monthly aggregation occurs. This preliminary organization of data ensures that when monthly summaries are generated, the detailed patterns are already captured and can be easily retrieved without requiring complex reprocessing, thus maintaining both simplicity and information completeness.
Data Source
AI summary
Utility demand is continuously monitored and monitoring data is aggregated and organized. The results are presented visually as a Utility Demand Footprint, referred to herein as a UDF. A UDF characterizes the utility demand in relation to selected influencing factors over a selected time period and over selected time intervals within the time period. In a preferred embodiment, the UDF is generated using a computer program and includes color mapping for simplifying analysis of the information displayed in the UDF. The footprint generation may be performed for a particular time period in which the demand essentially keeps its character (e.g., summer) or may be periodically updated (e.g., every day, every hour, etc.) to capture the latest changes.