COI optimizer
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Solution Overview
Problem
The energy industry faces challenges in understanding energy usage patterns and trading opportunities among users and providers, leading to inefficiencies and missed revenue opportunities due to a lack of effective communication and optimization tools.
Innovation Solution
The development of an energy optimizer system utilizing machine learning algorithms and dynamic transaction nexus for two-way communication between energy users and providers, enabling personalized alerts, demand response management, and energy trading platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional energy management systems are used, then infrastructure simplicity is maintained, but energy efficiency and revenue optimization are insufficient
Solution Approach 1:
The system segments energy management into multiple functional modules including machine learning algorithms for pattern recognition, dynamic transaction nexus for communication, and specialized optimization engines. This modular segmentation allows complex functionality to be distributed across independent components, improving energy efficiency through targeted optimization while managing overall system complexity through clear module boundaries.
Solution Approach 2:
The patent implements dynamic adjustment mechanisms where the system continuously adapts its operation based on real-time energy usage patterns, pricing signals, and trading opportunities. The dynamic transaction nexus enables flexible communication protocols that adjust to varying data requirements, allowing the system to optimize energy efficiency dynamically without requiring permanent complex infrastructure for all possible scenarios.
2Loss of information
If comprehensive data collection is implemented, then understanding of energy patterns improves, but loss of information and communication inefficiency increase
Solution Approach 1:
The system implements feedback mechanisms where machine learning algorithms continuously analyze energy usage patterns and provide actionable insights back to the optimization engine. This feedback loop enables the system to understand energy patterns comprehensively while minimizing communication overhead by only transmitting processed, relevant information rather than raw data, thus reducing both information loss and communication time.
Solution Approach 2:
The dynamic transaction nexus acts as an intermediary layer between data collection points and analysis engines. This intermediary processes and filters information locally, translating comprehensive data into condensed, meaningful patterns before transmission. This reduces communication time by minimizing data transfer requirements while maintaining comprehensive pattern understanding through intelligent local processing.
3Loss of energy
If real-time optimization is achieved, then energy costs are reduced, but device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary analysis using machine learning algorithms to identify energy usage patterns and predict future demands before optimization actions are required. By pre-processing data and establishing baseline patterns in advance, the system reduces real-time computational complexity while maintaining the ability to achieve real-time cost reductions through predetermined optimization strategies triggered by specific conditions.
Solution Approach 2:
The optimization system implements self-service mechanisms where the machine learning models continuously train and refine themselves using collected data without requiring external computational resources. The system autonomously adjusts its parameters and strategies based on incoming information, reducing the need for complex external computational infrastructure while maintaining real-time optimization capabilities that reduce energy costs.
Data Source
AI summary
Examples disclosed herein relate to an energy device including a memory, one or more processors, a transceiver, a display, and a dashboard. The memory includes one or more energy modules. The one or more processors are configured to communicate via the transceiver with one or more energy devices. The display is configured to display a dashboard of energy options based on one or more signals received from the one or more processors.