IoT Energy Flow Control via Predictive Allocation
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Solution Overview
Problem
Variations in energy generation from renewable resources lead to issues such as grid instability, intermittency, high energy storage costs, resource dependency, and energy demand mismatch.
Innovation Solution
A computer-implemented method and system that obtain real-time and predicted power generation and consumption data to determine predicted and actual energy allocation between energy source and sink nodes, generating control signals to manage energy flow and stabilize the grid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If renewable energy resources are utilized to generate electricity, then sustainable energy production is improved, but grid instability and intermittency occur due to variations in energy generation
Solution Approach 1:
The system performs preliminary actions by predicting future power consumption and generation patterns before they occur. The machine learning model analyzes historical data to forecast energy demands and generate allocation decisions in advance, allowing the system to prepare for and mitigate grid instability before it happens, rather than reacting after problems arise.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring real-time power generation and consumption data, comparing actual performance against predicted values, and adjusting allocation decisions accordingly. This closed-loop feedback allows the system to detect deviations from expected patterns and correct grid instability through dynamic reallocation of energy resources.
2Productivity
If real-time monitoring and prediction systems are implemented, then energy flow control is improved, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a single integrated platform that simultaneously performs data collection, real-time monitoring, historical analysis, machine learning predictions, and allocation decision-making. This universal approach consolidates multiple functions into one system, reducing overall complexity compared to having separate specialized systems for each function.
Solution Approach 2:
The system performs self-service through automated machine learning models that continuously improve their predictions by learning from historical data without requiring manual intervention. The system autonomously analyzes patterns, generates allocation decisions, and adjusts energy flow control parameters, eliminating the need for complex manual control mechanisms and reducing operational complexity.
3Productivity
If predicted allocation data is used to optimize energy distribution, then energy utilization is improved, but measurement and detection difficulty increases
Solution Approach 1:
The system replaces complex mechanical data collection and analysis mechanisms with automated machine learning algorithms. Instead of requiring sophisticated hardware systems for data acquisition and manual analysis, the system uses software-based machine learning models that automatically process data, detect patterns, and generate predictions, significantly simplifying the measurement and detection infrastructure.
Data Source
AI summary
Energy flow control is provided, which includes obtaining real time power generation data for each of one or more energy source nodes and obtaining real time power consumption data for one or more energy sink nodes. Further, predicted power consumption data is obtained for the one or more energy sink nodes, and predicted power generation data is obtained for each of one or more energy source nodes. Predicted allocation data is determined based at least on the predicted power consumption data, and the predicted power generation data. Actual allocation data is further determined based on the real time power consumption data, the real time power generation data, the predicted power consumption data, and the predicted power generation data. The actual allocation data is used for generating a first control signal.