Energy Asset Management Data Summarization for Faster EV Charging Decisions
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Solution Overview
Problem
Existing energy asset management systems face challenges such as high computational time and resource requirements, leading to missed deadlines and sub-optimal decisions, particularly due to the lack of consideration for external data like traffic conditions, which affects the accuracy of planning and performance.
Innovation Solution
The implementation of a data summarization algorithm to reduce the computational resources needed for energy asset management, allowing for faster and more accurate control decisions by creating a summarized dataset that represents the original data, enabling real-time or near-real-time operation and optimizing energy asset management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive measurements and data-driven approaches are used in energy asset management, then decision accuracy improves, but computational time and resource requirements increase
Solution Approach 1:
The patent extracts and separates the most critical features and data points from the comprehensive measurement dataset, focusing computational resources on the most influential variables for decision-making. This selective extraction maintains decision accuracy while reducing the overall computational burden by eliminating redundant or less impactful data processing.
Solution Approach 2:
The patent segments the computational process into distinct stages: data collection, feature extraction, model training, and decision generation. By dividing the comprehensive data processing into manageable segments, the system can process information more efficiently at each stage, reducing total computational time while preserving the accuracy benefits of comprehensive measurements.
2Measurement precision
If comprehensive measurements and data-driven approaches are used in energy asset management, then decision accuracy improves, but system complexity increases
Solution Approach 1:
The patent extracts only the essential features and data elements needed for accurate decision-making, rather than processing all available comprehensive measurements. This extraction approach maintains high decision accuracy while significantly simplifying the system architecture by removing unnecessary processing components and data management complexity.
3Speed
If control decisions are made with tight deadlines in real-time operation, then operational responsiveness improves, but decision quality deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-processing and extracting critical features from comprehensive measurements before real-time decisions are required. This advance preparation creates a streamlined dataset that can be quickly processed during tight deadlines, maintaining both operational responsiveness and decision quality by eliminating time-consuming processing steps beforehand.
Data Source
AI summary
Improvements in computer-based energy asset management technologies are provided. An energy asset management system with a data summarization mechanism can perform computations, for example relating to controlling the assets, which may include electric vehicles (EVs), with fewer computing resources. Further, the system can perform computations on large datasets where such computations would have otherwise been impractical with conventional systems due to the size of the data. A large dataset relating to the energy asset management system is reduced using the summarization mechanism, and a computation model is trained using the reduced dataset. Energy assets in the system may be controlled using the trained computational model. Assets may include EVs, and controlling the EVs may be based on generated predictions relating to charging interactions. The predictions may be based on road traffic information and/or weather related information. Further, the computational model may include an optimizer for scheduling charging interactions of EVs.