Building Energy Data Validation for Automated VEE Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Real-time and near real-time energy consumption systems face challenges in accurately processing energy data due to intermittent errors and anomalies, leading to suboptimal validation, estimation, and editing (VEE) techniques, which are often labor-intensive and less accurate due to the use of 'lightweight' VEE methods across multiple streams.
Innovation Solution
An automated VEE rules configuration engine dynamically optimizes validation, estimation, and editing techniques for individual energy consumption data streams, employing detection and estimation rules tailored to specific anomaly durations and stream types, reducing the need for manual data analyst intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lightweight VEE methods are used across multiple energy consumption data streams, then processing speed and system simplicity are improved, but data accuracy and reliability deteriorate due to intermittent errors and anomalies
Solution Approach 1:
The patent segments the energy consumption data processing into multiple specialized VEE techniques, each optimized for specific anomaly types and durations. Instead of applying a single lightweight method to all data streams, the system divides processing into targeted segments that address different error patterns, thereby improving accuracy without sacrificing overall processing efficiency.
Solution Approach 2:
The system dynamically selects and applies different VEE techniques based on the specific characteristics of each data stream and the detected anomaly type. This dynamic adaptation allows the system to optimize processing for each situation, improving accuracy while maintaining efficient throughput by avoiding unnecessary complex processing for clean data.
2Measurement precision
If manual data analyst intervention is increased to improve VEE accuracy, then data accuracy improves, but labor requirements and operational complexity increase
Solution Approach 1:
The system implements self-service through automated rule-based VEE techniques that independently detect, estimate, and correct anomalies in energy consumption data. The automated configuration engine and specialized VEE methods enable the system to perform data validation and correction without requiring manual analyst intervention, thereby maintaining high accuracy while reducing labor requirements.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational VEE techniques. The system uses algorithmic methods including detection rules, estimation models, and editing algorithms to substitute human analysts, achieving comparable or superior accuracy while eliminating the need for manual labor in routine data validation tasks.
3Productivity
If automated VEE rules configuration is implemented, then labor requirements decrease and processing efficiency improves, but system complexity and initial setup requirements increase
Solution Approach 1:
The system performs preliminary action by pre-configuring multiple specialized VEE techniques and detection rules before actual data processing begins. The automated configuration engine prepares the processing framework in advance, establishing the rules and parameters needed for efficient automated operation, which reduces complexity during runtime despite the sophisticated capabilities.
Solution Approach 2:
The patent implements a universal automated configuration engine that can handle multiple types of energy consumption data streams and anomaly patterns through a single integrated system. This multi-functional approach consolidates what would otherwise require multiple separate systems, managing complexity through unification while providing comprehensive automated VEE capabilities across diverse data sources.
Data Source
AI summary
A building control system that includes a post VEE readings data stores, a rules processor, and a building controller. The stores provide tagged data sets that are each associated with a corresponding one of energy consumption streams, each of the sets having groups of contiguous interval values tagged as having been validated. The rules processor reads the stores and creates anomalies having different durations using only the groups of contiguous interval values and generates estimates for the anomalies by employing estimation techniques for each of the different durations and selects one of the estimation techniques for subsequent employment. The building controller receives post VEE readings and outside temperatures corresponding to the interval-based streams and determines and controls cumulative energy consumption corresponding to the interval-based streams and manages the cumulative energy consumption by scheduling run times for building elements that are coupled to the building controller.