Utility Expenditure Heatmaps for Accurate Address-Level Forecasting
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Solution Overview
Problem
Current methods for estimating utility costs are inaccurate as they rely on utility rates and do not account for various factors and circumstances, leading to unexpected utility expenses for customers.
Innovation Solution
A computer-implemented method and system that uses historical transactional data from multiple customers to generate a heatmap indicating estimated utility expenditures, incorporating demographic and usage patterns, and provides recommendations based on machine learning models to users for more accurate forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current utility rate-based estimation methods are used, then the estimation process is simple, but the prediction accuracy is poor
Solution Approach 1:
The patent combines multiple data sources (utility rates, demographic data, climate data, property characteristics) and multiple estimation methods into a single comprehensive system. This merging of diverse elements resolves the contradiction by creating a unified approach that achieves high prediction accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The system performs multiple functions: it estimates utility costs, compares predictions with actual data, identifies anomalies, and provides recommendations. This multi-functionality resolves the contradiction by creating a versatile platform that delivers accurate predictions across different utility types and scenarios without requiring separate specialized systems.
2Adaptability or versatility
If utility rates alone are used for estimation, then the system is easy to operate, but it does not account for various factors and circumstances
Solution Approach 1:
The system automatically collects and processes diverse data including utility rates, demographic information, climate data, and property characteristics without requiring user input for each factor. This self-service approach resolves the contradiction by enabling comprehensive factor coverage while maintaining ease of operation, as the system autonomously gathers and integrates all necessary data.
Solution Approach 2:
The system pre-collects and stores utility rate data, demographic data, climate data, and property characteristics before estimation is needed. This preliminary action resolves the contradiction by having all factors ready in advance, allowing the system to provide comprehensive predictions without requiring users to manually gather or input diverse information at the time of use.
3Measurement precision
If historical transactional data from multiple customers is collected and processed, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent replaces manual or simple mechanical data processing with automated electronic systems that use algorithms and machine learning models to analyze historical transactional data. This substitution resolves the contradiction by enabling complex data processing tasks to be performed automatically, achieving high prediction accuracy from multiple customer data sources without proportionally increasing operational complexity.
Solution Approach 2:
The system creates standardized data models and templates for processing historical transactional data, demographic information, and climate data. By using consistent copying mechanisms for data structures and processing algorithms across different utility types and locations, the system achieves high prediction accuracy from diverse data sources while reducing processing complexity through standardization.
Data Source
AI summary
A computer-implemented method for providing an estimated utility expenditure to a user may include: obtaining, via one or more processors, historical transactional data of one or more customers other than the user from one or more transactional entities, wherein the historical transactional data includes: at least one address of a given customer of the one or more customers; and a historical utility expenditure associated with the at least one address; generating, via the one or more processors, a heatmap based on the historical transactional data of the one or more customers via one or more algorithms, wherein the heatmap is indicative of at least the estimated utility expenditure associated with the at least one address during a predetermined period; and causing a display of a user device associated with the user to demonstrate the heatmap.