Energy Consumption Index Generation Using Hierarchical Data Segmentation
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Solution Overview
Problem
Current systems lack the ability to provide granular energy consumption data, making it difficult for individuals, households, and governments to effectively reduce energy consumption and align personal efforts with larger public or national energy conservation goals, as macro-level data is not detailed enough for creating effective conservation programs.
Innovation Solution
The development of systems and methods to uniformly track and communicate energy consumption through the generation of energy indices for individuals, households, businesses, and organizations, using data collectors that aggregate resource consumption data from various sources, including appliances, vehicles, and purchases, to provide granular insights into energy usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If macro-level energy consumption data is used, then government policy-making is simplified, but the detail and granularity required for effective conservation programs is lost
Solution Approach 1:
The patent segments energy consumption data into multiple levels of granularity - from macro-level aggregate data for policy-making to micro-level appliance-specific data for conservation programs. The system divides the energy consumption measurement into hierarchical categories (e.g., total consumption, sector-level, building-level, appliance-level) allowing users to access appropriate detail levels for different purposes.
Solution Approach 2:
The patent adds a dimensional layer to energy consumption data by introducing temporal resolution and categorical segmentation. Instead of a single macro statistic, the system provides data across multiple dimensions (time-based aggregation levels, sector classifications, building types, appliance categories) enabling analysis at any desired granularity level.
2Measurement precision
If granular energy consumption data is collected from multiple sources, then conservation program effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal data collection framework that can accommodate multiple energy consumption sources (electrical meters, water meters, gas meters, appliance-level sensors) through a common processing architecture. The system uses standardized protocols and a unified data management platform that handles diverse data types consistently, reducing the complexity burden of managing multiple specialized systems.
Solution Approach 2:
The patent introduces an intermediary data processing layer between various energy consumption sources and the final analysis applications. This intermediate system standardizes, validates, and integrates data from multiple sources before making it available to users, shielding the complexity of data harmonization from both the data collection infrastructure and the end-user applications.
3Adaptability or versatility
If energy consumption is tracked at individual and household levels, then conservation program targeting is improved, but data aggregation and processing requirements increase
Solution Approach 1:
The patent segments the data processing workload by implementing hierarchical aggregation - processing and storing data at multiple levels (individual, household, building, sector) simultaneously. This allows the system to serve both highly targeted individual-level programs and broader sector-level initiatives without requiring separate processing systems for each level.
Solution Approach 2:
The patent performs preliminary data aggregation and pre-processing at the time of data collection, organizing data into hierarchical structures before it reaches final analysis systems. This preliminary structuring reduces the processing burden during later analysis phases and enables flexible querying at any aggregation level without reprocessing the entire dataset.
Data Source
AI summary
Systems, methods, and apparatus to generate an energy consumption index are disclosed. In one described example, a method to generate an energy consumption index is disclosed, the method including measuring resources consumed at a home associated with a first person and calculating a home energy value indicative of the resources consumed at the home and measuring resources consumed by the person while located outside the home and calculating an out of home energy value indicative of the resources consumed by the person while located outside the home. The example method further includes identifying goods purchased by the person and calculating a purchase energy value indicative of the resources associated with the purchased goods, and generating an energy consumption index associated with the person based on the home energy value, the out of home energy value, and the purchase energy value.