Energy Data Visualization for Cross-Site Consumption Normalization
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Solution Overview
Problem
Existing energy management systems face challenges in efficiently comparing and analyzing energy consumption across multiple sites with varying equipment levels, making it difficult to identify energy usage issues and opportunities for cost savings.
Innovation Solution
A system that uses alpha and beta factors to normalize energy-related data, allowing for automatic comparison of energy consumption across sites, and provides diagnostic visualizations to support energy analysts in identifying outliers and optimizing energy use by integrating graphical user interfaces and dashboards for energy-related information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If energy consumption data from multiple sites with varying equipment levels is compared directly, then the comparison process is simple, but the accuracy of energy usage analysis is poor
Solution Approach 1:
The patent transforms raw energy consumption data into normalized metrics by applying multiple transformation parameters: (1) dividing by equipment count to get per-equipment averages, (2) applying seasonal adjustment factors to account for temporal variations, (3) using site-specific baseline values to remove location-based biases. This multi-parameter transformation enables accurate cross-site comparison while accounting for equipment level differences.
Solution Approach 2:
The patent introduces intermediate normalized metrics as mediators between raw consumption data and final analysis results. These intermediates include: (1) equipment-level normalized consumption, (2) seasonally-adjusted deviations, (3) baseline-corrected values. These intermediate representations enable systematic comparison across sites with different equipment configurations.
2Loss of information
If detailed diagnostic visualizations are provided for all sites, then the completeness of energy analysis is improved, but the time required to identify issues increases
Solution Approach 1:
The patent segments the enterprise into hierarchical levels: (1) enterprise-wide aggregated view showing overall patterns, (2) site-level summaries displaying key metrics for each location, (3) equipment-level detailed data available on-demand. This segmentation allows analysts to quickly assess the enterprise state at the aggregate level and drill down to specific sites or equipment only when anomalies are detected, significantly reducing time to identify issues while maintaining complete analytical capability.
Solution Approach 2:
The patent adds temporal and hierarchical dimensions to the visualization system. Temporally, it provides aggregated views (monthly, quarterly, yearly) alongside detailed daily data. Hierarchically, it organizes data from enterprise-level summaries down to site and equipment-level details. This multi-dimensional organization enables analysts to navigate from broad patterns to specific issues efficiently.
3Adaptability or versatility
If normalization factors are applied across all sites, then the comparability of energy data is improved, but the computational resources required increase
Solution Approach 1:
The patent performs normalization computations in advance and stores pre-calculated normalized values and seasonal adjustment factors. When queries are executed, the system retrieves these pre-computed values rather than performing full normalization calculations in real-time. This preliminary computation approach maintains high data comparability while significantly reducing the computational energy required during actual analysis operations.
Data Source
AI summary
A system and approach for diagnostic visualizations of, for example, building control systems data. A focus may be on a similarity metric for comparing operations among sites relative to energy consumption. Normalizing factors may be used across sites with varying equipment consumption levels to be compared automatically. There may also be a high level overview of an enterprise of sites. For instance, consumption totals of the sites may be normalized by site size and length of time of a billing period to identify such things as outlier sites. One may use a main view of geographic distribution dynamically linked to subviews showing distribution by size, by aggregated climate, and so on. With these views, one may quickly drill through the enterprise and identify sites of interest for further investigation. A key metric may be intensity which invokes viewing virtually all sites by normalized consumption for a unit amount of time.