Cloud Energy Profile Identification via Consumption Pattern Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
End users face difficulties in accurately estimating their energy consumption charges due to varying energy usage patterns influenced by external factors, and existing methods are user-unfriendly and unreliable, especially with energy profile information typically available only in textual form.
Innovation Solution
A cloud-based method and system that obtain total energy consumption data, estimate consumption profiles for time slots, and match these profiles with utility provider schedules to calculate accurate energy charges by minimizing error, using a processor with modules for billing data, consumption profile estimation, peak load estimation, and error calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If energy profile information is obtained from textual contract form, then information availability is improved, but extraction reliability deteriorates
Solution Approach 1:
The patent creates a digital copy of the energy profile information by matching actual consumption patterns against template profiles. Instead of manually extracting text, the system copies the essential characteristics of energy profiles through pattern recognition and statistical matching, transforming unstructured textual contracts into structured, machine-readable consumption patterns.
Solution Approach 2:
The patent replaces manual text extraction mechanics with automated computational methods. The system uses algorithms to automatically analyze consumption data, identify patterns, and match profiles without human intervention, substituting mechanical text processing with intelligent data analysis systems that improve both speed and reliability.
2Measurement precision
If detailed monitoring of current energy consumption is implemented, then estimation accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The patent introduces consumption profiles as an intermediary layer between raw monitoring data and final estimates. Instead of directly processing complex detailed monitoring data, the system uses pre-defined profile templates as mediators that simplify the analysis. These profiles act as reference models that translate detailed consumption patterns into understandable, actionable estimates.
Solution Approach 2:
The patent transforms detailed monitoring data by changing its representation parameters. The system aggregates fine-grained consumption data into profile characteristics such as peak demand timing, average consumption rates, and usage patterns. This parameter transformation reduces data complexity while preserving essential information needed for accurate estimation.
3Ease of operation
If assumption of constant billed amount is used for estimation, then simplicity is improved, but accuracy deteriorates
Solution Approach 1:
The patent transitions from static constant assumptions to dynamic profile-based estimation. Instead of assuming billed amounts remain fixed, the system uses consumption profiles that capture temporal variations in usage patterns. These dynamic profiles adjust estimates based on time-of-day, day-of-week, and seasonal patterns, allowing the system to adapt to changing consumption behaviors while maintaining operational simplicity.
Data Source
AI summary
A cloud-based method for identifying a energy profile of an end user is provided. The method includes obtaining information including a total amount of consumed energy and a total equivalent for at least one previous period from at least one listing of the end user, and estimating a consumption profile for a plurality of time slots based on the total amount of consumed energy. The method further includes obtaining a plurality of rate schedules provided over a data network, each schedule corresponding to a energy profile provided by a utility provider, calculating an estimated equivalent for each energy profile based on the estimated consumption profile and schedule corresponding to each energy profile, calculating an error between obtained total equivalent for the period and each one of the calculated estimated equivalent for each energy profile, and selecting a energy profile from the plurality of energy profiles corresponding to the smallest calculated error as the energy profile most likely used by the end user. A cloud-based method for estimating energy consumption charges of an end user and a cloud-based energy profile identification system are also disclosed.