Bill Comparison Apparatus for Energy Cost Attribution
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Solution Overview
Problem
Conventional systems fail to accurately attribute changes in energy costs between billing cycles to specific factors, such as changes in energy prices and rate plans, leading to misunderstandings among energy users and inefficiencies in energy consumption management.
Innovation Solution
A system and method that itemize the contributions of seasonal and non-seasonal rate changes, as well as changes in rate plans, to the total energy cost by creating a clone of billing data from one cycle and comparing it to another, allowing for a detailed breakdown of factors influencing energy costs, which can be transmitted to energy users for informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems itemize factors contributing to energy cost changes based solely on quantity of energy consumed, then the data structure is simple to generate, but the accuracy of identifying reasons behind cost changes deteriorates
Solution Approach 1:
The patent segments the energy cost change analysis into multiple independent factors: quantity of energy consumed, price per unit energy, billing period duration, and rate plan changes. Each factor is calculated and presented separately in the itemized breakdown, allowing users to understand the specific contribution of each element to the overall cost change.
Solution Approach 2:
The system introduces an intermediary processing layer that receives raw billing data from utility companies and transforms it into a structured, itemized breakdown. This intermediary system performs the complex calculations and data organization, then presents the results in a user-friendly format through web portals or mobile devices.
2Reliability
If conventional systems do not consider factors beyond energy quantity, then the system operation is simple, but the reliability of cost attribution deteriorates
Solution Approach 1:
The patent divides the cost attribution into distinct segments: energy consumption quantity, price per unit energy variations, billing period length effects, and rate plan change impacts. Each segment is analyzed independently and contributes to the overall reliable attribution of cost changes.
Solution Approach 2:
The system provides feedback to users through itemized breakdowns that show the specific contribution of each factor to their energy cost changes. This feedback mechanism allows users to understand the reliability of the attribution by seeing the detailed composition of cost changes, including factors beyond just energy quantity.
3Ease of operation
If detailed itemized breakdowns are provided to energy users, then user understanding and optimization capability improve, but the complexity of data processing and distribution increases
Solution Approach 1:
The system creates a simplified copy or representation of the complex billing data in the form of an itemized breakdown. This copy presents the essential information about cost drivers in an easily understandable format, enabling users to make informed decisions without needing to process the underlying complex data structures.
Solution Approach 2:
An intermediary system handles the complex data processing and distribution tasks, transforming raw utility billing data into user-friendly itemized breakdowns. This intermediary layer manages the complexity of data distribution through web portals and mobile devices while presenting simplified information to end users.
Data Source
AI summary
Systems, methods, and other embodiments associated with controlling a comparison of energy costs for a current and a previous billing cycle are described. In one embodiment, current billing data and previous billing data are received. A variable having a different value for the current billing than for the previous billing data is identified. A value of the variable in the current billing data is modified with a value of the variable in the previous billing data to determine a hypothetical energy cost for the current billing cycle based on the value of the variable in the previous billing data. A cost difference between the total cost of energy for the current billing cycle and the hypothetical energy cost is determined, and a personalized data structure for an energy user is generated. The data structure includes an individual contribution of the modified value of the variable toward the cost difference.