AI Cognitive Framework for Energy Source Selection
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Solution Overview
Problem
Power-transmission companies face challenges in accurately matching fluctuating consumer demand with available energy supplier capacity, especially during peak and burst periods, due to unpredictable demand and supplier power-generating capacities, leading to potential power shortages and suboptimal contractual terms.
Innovation Solution
A blockchain-based system utilizing an artificially intelligent cognitive framework with a sliding-frame mechanism to analyze historical demand patterns and supplier data, ranking energy suppliers based on their ability to meet projected demand, and selecting an optimal mix to ensure cost-effective power delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional power-matching methods are used, then operational simplicity is maintained, but demand-supply matching accuracy deteriorates during peak and burst periods
Solution Approach 1:
The patent segments the power-matching process into distinct modules: blockchain data retrieval module, AI cognitive framework module with sliding-frame mechanism, supplier ranking module, and optimal mix selection module. Each module handles specific aspects of the complex matching process, improving accuracy while managing system complexity through functional decomposition.
Solution Approach 2:
The system performs preliminary actions by pre-analyzing historical demand patterns and supplier capacities using the sliding-frame mechanism on blockchain data before actual power-matching occurs. This advance preparation enables more accurate real-time matching during peak and burst periods without increasing operational complexity during critical moments.
2Reliability
If simple routing methods are used, then system complexity is reduced, but reliability of power delivery during peak periods deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the AI cognitive framework continuously monitors demand patterns, supplier performance, and grid conditions. The sliding-frame mechanism analyzes historical outcomes to refine future routing decisions, creating a closed-loop system that improves reliability through iterative learning while managing complexity through automated adaptation.
Solution Approach 2:
The system dynamically changes routing parameters based on real-time conditions, including supplier capacity thresholds, demand prediction confidence levels, and contractual term priorities. The AI framework adjusts these parameters automatically according to grid conditions, improving reliability during peak periods without requiring manual intervention or overly complex fixed-rule systems.
3Productivity
If comprehensive data analysis is performed, then cost-effectiveness of power delivery is improved, but data processing time increases
Solution Approach 1:
The system performs comprehensive data analysis in advance using the sliding-frame mechanism to identify recurring demand patterns and predict future requirements. By pre-processing blockchain data and establishing baseline expectations, the system reduces real-time processing needs while maintaining cost-effectiveness through informed decision-making about supplier selection and routing optimization.
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with AI cognitive frameworks that can analyze complex patterns more efficiently. The sliding-frame mechanism uses intelligent algorithms to identify relevant patterns in blockchain data without requiring exhaustive analysis of all historical records, reducing processing time while maintaining comprehensive evaluation of cost-effectiveness factors.
Data Source
AI summary
A power-distribution routing system of a power-transmission company receives a request to route electrical power to users through a power-grid infrastructure during a specified future period of time. The system retrieves time-stamped blockchain data that identifies past fluctuations in energy demand, service agreements between energy companies, and energy-production and demand-fulfilment histories of energy-generating sources like power plants. The system also retrieves extrinsic contextual and socioeconomic data from online sources and various business applications. An artificially intelligent cognitive framework uses a sliding-frame mechanism to infer patterns in the rate of change of user demand during past time periods similar to the period specified by the request. The system ranks each source by its demonstrated ability to satisfy the patterns of demand in consideration of the contextual data. The system directs downstream components to route energy from a mix of the highest-ranking suppliers through the grid during the specified time period.