Fluid Resource Forecasting via Regression Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining fluid resource needs, such as natural gas and liquefied petroleum, are inefficient, relying on monthly meter readings and taking up to 14 months to complete a true-up cycle, which burdens utility companies and does not accurately match with standard gas day periods.
Innovation Solution
Implementing a system with a supply controller that uses smart meters to collect and analyze usage data, temperature data, and regression analysis to forecast gas needs, allowing for real-time adjustments and deliveries within a 48-hour window.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If monthly meter readings are used to determine fluid resource needs, then the measurement process is simple, but the response time is too slow (up to 14 months for true-up cycle)
Solution Approach 1:
The system performs preliminary forecasting of fluid resource needs using regression analysis on historical data before actual consumption occurs. This allows the utility company to pre-position resources and reduce the true-up cycle from 14 months to 48 hours by having resources ready in advance based on predicted demand patterns.
Solution Approach 2:
The system implements continuous feedback loops where smart meter readings are constantly collected, analyzed against regression models, and used to adjust resource delivery in real-time. This feedback mechanism enables the system to respond to actual consumption patterns dynamically, dramatically reducing the response time while maintaining manageable complexity through automated control algorithms.
2Measurement precision
If smart meters are deployed to enable frequent readings, then the data collection frequency increases, but the alignment with standard gas day periods remains misaligned
Solution Approach 1:
The system dynamically adjusts the timing and aggregation of smart meter readings to align with standard gas day periods. Rather than using fixed midnight readings that don't match gas day boundaries, the system flexibly aggregates data over the actual gas day period (typically 8 am to 8 am the next day), ensuring temporal alignment between measurement and billing cycles while maintaining high measurement precision.
3Measurement precision
If regression analysis with temperature data is used to forecast gas needs, then the forecasting accuracy improves, but the computational complexity increases
Solution Approach 1:
The system changes the parameters used in forecasting by incorporating temperature data as a key variable in regression analysis. This allows the model to account for seasonal and weather-related variations in gas consumption, significantly improving forecasting accuracy. The complexity is managed by using established regression techniques and pre-processing temperature data into relevant features.
Data Source
AI summary
Methods and systems for providing fluid resources are disclosed. A system or method may comprise developing an accurate fluid resource need that allows for expedited true up of fluid resource deliver from resource suppliers to a utility provider. The system and methods for delivering a fluid resource may include calculating a regression line based on fluid resource usage, and an associated temperature during the usage period, and a forecasted temperature for a future time period when the fluid resource will needed. The regression line may be used to obtain the amount fluid resource needed from resource suppliers for deliver by a utility provider to customers.