Smart Meter Data Segmentation for Appliance-Level Energy Attribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing energy management tools can only provide total electrical usage data from building meters, lacking specificity to diagnose energy usage issues effectively, limiting their usefulness in energy conservation efforts.
Innovation Solution
A software tool that converts energy usage data from building meters into detailed information by incorporating building characteristics, weather data, and device usage patterns, allowing for precise identification of energy consumption by devices and time, using a JAVA-based program that calculates energy usage by dividing the day into distinct periods and iteratively matching calculated loads to meter readings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If total electrical usage data is provided from building meters, then energy management information is available, but the data lacks specificity for effective diagnosis of energy usage issues
Solution Approach 1:
The patent segments total electrical usage data into individual appliance-level consumption data by dividing the day into distinct periods (off-peak, mid-peak, on-peak) and using iterative calculations to attribute energy consumption to specific devices based on their operational patterns and characteristics
Solution Approach 2:
The software acts as an intermediary that processes raw meter data through multiple calculation stages, using building characteristics, weather data, and device usage patterns as intermediate variables to transform aggregate energy data into appliance-specific consumption information
2Productivity
If detailed energy usage information by device and time is provided, then energy management effectiveness is improved, but data processing and analysis complexity increases
Solution Approach 1:
The software dynamically adjusts its analysis by dividing the day into different time periods and using iterative calculations that adapt to varying building characteristics, weather conditions, and device usage patterns to produce accurate appliance-level energy attribution
Solution Approach 2:
The system changes multiple parameters simultaneously including time period divisions, building characteristics (square footage, sun angles, temperatures), weather data, and device usage patterns to transform raw energy data into meaningful appliance-specific consumption information
Data Source
AI summary
A computer implemented method for energy management in a building by taking information on energy usages of a metered building and matching the energy usage to calculated energy usages for electrical devices.