LSTM Power Usage Prediction for Threshold-Based Energy Alerts
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Solution Overview
Problem
The power generation industry faces challenges in energy conservation due to human error, such as failure to switch off electrical equipment and inadequate maintenance, leading to energy wastage and equipment breakdowns, which can be addressed by implementing a predictive power usage monitoring system using a Long Short-Term Memory (LSTM) neural network to analyze power consumption patterns and alert users to high consumption levels.
Innovation Solution
A power usage prediction system utilizing an LSTM neural network trains on historical data to generate predictions for future power consumption, compares these predictions to user-defined thresholds, and alerts users to potential power usage conditions, enabling actions to conserve power and maintain safety through remote device control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional manual monitoring of power consumption is used, then users can directly observe and control power usage, but human error leads to energy wastage and equipment breakdowns
Solution Approach 1:
The system enables self-service monitoring where the LSTM neural network automatically tracks power consumption patterns, detects anomalies, and sends alerts without requiring continuous human intervention. The system serves itself by autonomously identifying high consumption events and notifying users, eliminating manual monitoring errors while maintaining equipment safety through automated surveillance.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring power consumption data, comparing it against learned patterns, and providing real-time notifications to users when abnormal consumption is detected. This closed-loop feedback enables users to take corrective actions promptly, preventing energy wastage and potential equipment failures while maintaining operational reliability.
2Loss of information
If predictive monitoring using LSTM neural network is implemented, then power consumption patterns can be analyzed and high usage can be predicted, but system complexity increases
Solution Approach 1:
The patent replaces traditional mechanical or manual monitoring systems with an intelligent LSTM neural network-based predictive system. This substitution transforms the monitoring approach from reactive and manual to proactive and automated, enabling deep analysis of power consumption patterns while managing complexity through software-based intelligence rather than hardware complexity.
Solution Approach 2:
The system changes the monitoring parameters from simple threshold-based alerts to sophisticated pattern recognition using LSTM networks. By analyzing temporal patterns, consumption rates, and device behavior over time, the system extracts meaningful insights from power consumption data while managing complexity through algorithmic approaches rather than increasing hardware sophistication.
3Measurement precision
If continuous monitoring of all devices is performed, then complete power usage visibility is achieved, but energy consumption for monitoring increases
Solution Approach 1:
The system applies partial monitoring action by focusing computational resources on detecting significant consumption events and anomalies rather than continuously analyzing every data point with equal intensity. The LSTM network learns to identify critical patterns and triggers alerts only when necessary, achieving high measurement precision for power usage while minimizing the energy consumed by the monitoring system itself through selective, event-driven analysis.
Data Source
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AI summary
A power usage prediction system implements a long short term memory (LSTM) neural network to receive power usage inputs and generate predicted values of power consumption for a plurality of devices. A user provides configuration input regarding the time steps at which the predicted values are to be generated and the various devices for which the predicted values of power consumption are desired. Whenever a power usage input is received, the LSTM neural network outputs the corresponding hidden state values for a plurality of time steps as the predicted values. The hidden state values are each compared to a final cell state value corresponding to a power consumption threshold of the time interval which includes the time steps. Based on the comparison, a power usage condition is recorded. Various actions to mitigate the high power consumption can be implemented in response to recording the power usage condition.