Energy Scenario Comparison for Faster Site Efficiency Evaluation
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Solution Overview
Problem
Analyzing energy efficiency in industrial or commercial sites is a complex, time-consuming process due to the need to combine data from various sources and create a suitable physical model, making it difficult to accurately optimize energy consumption.
Innovation Solution
A method that compares a second energy consumption scenario with a first scenario to output a quality measure, allowing for controlled power consumption based on similarities in energy data, using an energy management system and potentially automated or semi-automated processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to evaluate energy efficiency, then deep understanding of the site can be achieved, but the process becomes time-consuming
Solution Approach 1:
The patent creates virtual copies of energy consumption scenarios through digital twins that replicate the behavior and energy patterns of physical devices. These digital models can be analyzed rapidly without requiring manual site visits or detailed physical measurements, thus reducing analysis time while maintaining evaluation accuracy through sophisticated simulation algorithms.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing energy consumption data in structured databases before actual analysis is needed. Energy scenarios are pre-simulated and stored as reference models, allowing rapid comparison and evaluation when assessment is required, eliminating the need for time-consuming manual data collection and processing during the actual evaluation.
2Measurement precision
If data from different data sources are combined for comprehensive analysis, then accurate energy consumption analysis is achieved, but the complexity of the process increases
Solution Approach 1:
The patent introduces an intermediary layer in the form of an energy management system that acts as a mediator between various data sources and the analysis process. This intermediary system standardizes data formats, harmonizes different measurement units, and integrates diverse data streams through unified protocols, thereby reducing the complexity of combining data from multiple sources while maintaining comprehensive analysis accuracy.
Solution Approach 2:
The system employs universal data structures and standardized interfaces that can handle multiple types of energy data (electrical, thermal, mechanical) from various sources through a single unified framework. This multi-functional approach allows the same data integration mechanism to process different data types and sources without requiring separate complex integration processes for each data type.
3Productivity
If physical models are created for energy optimization, then optimization potential can be identified, but obtaining accurate physical models becomes inherently difficult
Solution Approach 1:
Instead of creating complex physical models from scratch, the patent uses digital copies of existing energy scenarios and performance data. These digital twins are generated by replicating historical operation data and using machine learning algorithms to create virtual representations that capture the essential energy behaviors without requiring detailed physical modeling, thus reducing model creation difficulty while maintaining optimization capability.
Solution Approach 2:
The system enables self-service model generation where the energy management system automatically creates and updates energy scenarios using its own collected operational data. The system self-adjusts and refines its models continuously by learning from actual device performance, eliminating the need for manual expert intervention in model creation and reducing the inherent difficulty of obtaining accurate physical models.
Data Source
AI summary
A method for evaluating an energy efficiency of a second energy consumption scenario of a site includes obtaining a first energy consumption scenario, which comprises a first time-series of energy consumption data of at least one device, and a quality measure of the first energy consumption scenario; obtaining the second energy consumption scenario, which comprises a second time-series of energy consumption data, wherein the second energy consumption scenario has a same or a shorter duration than the first energy consumption scenario; comparing the second time-series of energy consumption data to the first time-series of energy consumption data; and if or when the second time-series of energy consumption data is similar to the first time-series of energy consumption data, outputting the quality measure of the first energy consumption scenario.