Battery Temperature Prediction for Energy Storage Anomaly Detection
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Solution Overview
Problem
Energy storage systems face inefficiencies and anomalies that can lead to degradation or outages due to unmonitored changes in operational parameters, necessitating improved anomaly detection and optimization of energy dispatch.
Innovation Solution
Implementing a system that uses a computing device to generate energy dispatch patterns based on market and environmental data, determine state variables, estimate marginal degradation, and adjust energy storage units, while employing machine learning for anomaly detection by comparing predicted and measured temperatures to detect anomalies and adjust usage accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anomaly detection methods are used in energy storage systems, then the system structure remains simple, but the detection accuracy and scope are insufficient leading to undetected anomalies
Solution Approach 1:
A machine learning model serves as an intermediary between raw operational data and anomaly detection. The model processes usage data, predicts expected temperatures, and flags deviations as anomalies, enabling accurate detection without complex manual monitoring systems
Solution Approach 2:
Traditional mechanical or rule-based anomaly detection is replaced with a machine learning-based predictive system. The system substitutes simple threshold checking with intelligent prediction models that analyze patterns and predict expected behavior, significantly improving detection accuracy
2Reliability
If energy storage units operate without optimization, then operational simplicity is maintained, but degradation occurs leading to reduced system reliability
Solution Approach 1:
The system performs preliminary actions by predicting future temperatures and degradation trends before they occur. By analyzing usage data and predicting outcomes, the system proactively adjusts energy dispatch patterns to prevent degradation and maintain reliability
Solution Approach 2:
A feedback mechanism continuously monitors actual temperature and usage data, compares it with predicted values, and adjusts energy dispatch patterns accordingly. This closed-loop control optimizes reliability by learning from actual system behavior and adapting operations
Data Source
AI summary
Methods, systems, apparatuses, and non-transitory computer-readable media are provided for anomaly detection in energy storage systems. In one implementation, the computer-readable media includes instructions to cause a processor to: receive usage data of a battery located within one or more energy storage units during a time period; input the usage data to a machine learning model; generate, based on processing of the usage data by the machine learning model, a predicted temperature of the battery at the end of the time period; receive, from a temperature sensor of the battery, a measured temperature of the battery at the end of the time period; determine a difference between the predicted temperature and the measured temperature; based on the determined difference, send an indication of a state of the battery; and based on the state of the battery, configure usage of the battery.