AI-Guided Forward Energy Transactions for Machine Fleets
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Solution Overview
Problem
There is a need for systems and methods that improve the efficiency, speed, and reliability of machines involved in market activities, particularly in distributed ledger transactions, energy, compute, and resource allocation, amidst the challenges of energy-intensive computing operations and volatility in resource markets.
Innovation Solution
A transaction-enabling system that includes a resource requirement circuit, a forward resource market circuit, and a controller with AI capabilities to aggregate resource needs, configure transactions on energy markets, and iteratively improve task outcomes using machine learning and neural networks for optimal energy and compute resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machines perform high-performance computing operations for market activities and distributed ledger transactions, then processing speed and transaction capability are improved, but energy consumption increases significantly
Solution Approach 1:
The system performs preliminary actions by forecasting energy prices and computing resource requirements in advance before actual transactions occur. The forward market contracts allow the system to pre-commit to energy purchases at predicted prices, reducing the need for high-performance real-time computing during actual transactions and thereby lowering energy consumption while maintaining productivity.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual energy consumption, transaction outcomes, and market conditions. This feedback is used to refine machine learning models and improve forecasting accuracy over time, enabling more efficient resource allocation and reduced energy waste in high-performance computing operations.
2Productivity
If the system accesses forward markets and performs complex transaction optimization, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces intermediary components including machine learning forecasting models and smart contract wrappers that mediate between complex market data and transaction execution. These intermediaries simplify the overall system architecture by handling complex computations and market interactions in standardized ways, improving resource allocation efficiency without proportionally increasing system complexity.
Solution Approach 2:
The system segments complex transaction optimization into separate functional modules: market data collection, forecasting models, transaction configuration, and execution. This segmentation allows each component to be optimized independently and improves overall resource allocation efficiency while managing system complexity through modular design.
3Reliability
If the system uses machine learning and neural networks for iterative improvement, then task outcome quality is improved, but computing energy requirements increase
Solution Approach 1:
The system implements periodic action by training machine learning models and neural networks at scheduled intervals rather than continuously. Forecasting models are retrained periodically with new data, and transaction optimization parameters are updated at regular intervals. This periodic approach maintains high task outcome quality through iterative improvement while significantly reducing computing energy consumption compared to continuous training operations.
4Use of energy by moving object
If the system aggregates resource requirements for fleets of machines and optimizes forward market transactions, then overall energy utilization is improved, but transaction configuration complexity increases
Solution Approach 1:
The system implements universality by creating standardized transaction configuration templates and smart contract wrappers that can be applied across diverse fleets of machines and different forward market contracts. These universal interfaces simplify transaction configuration by providing consistent methods for aggregating resource requirements and executing optimizations, improving energy utilization without proportionally increasing configuration complexity.
Data Source
AI summary
Systems and methods for machine forward energy transactions optimization are disclosed. A transaction-enabling system may include a resource requirement circuit to aggregate a resource requirement for a fleet of machines to perform a task, a forward resource market circuit to access a forward market for energy, and a controller. The controller may include an artificial intelligence (AI) circuit to configure a transaction on the forward market for energy in response to the aggregated resource requirement and a machine resource acquisition circuit to automatically solicit the configured transaction on the forward market for energy. The AI circuit may also iteratively improve the configured transaction to improve a task outcome of the fleet of machines.