Energy storage power prediction error

Why is predicting voltage anomalies important in energy storage stations?

Early and precise prediction of voltage anomalies during the operation of energy storage stations is crucial to prevent the occurrence of voltage-related faults, as these anomalies often indicate the possibility of more serious issues.

Can neural network models predict battery voltage anomalies in energy storage plant?

Based on the pre-processed dataset, the Informer and Bayesian-Informer neural network models were used to predict battery voltage anomalies in the energy storage plant. In this study, the dataset was divided into training and test sets in the ratio of 7:3.

What happens when errors accumulate during the forecasting phase?

Throughout the forecasting phase, errors progressively accumulate, resulting in deviations between subsequent predicted values and actual values. Figure 6 b illustrates the absolute errors in prediction results relative to experimental data.

What is the voltage range of energy storage power station?

The range of abnormal voltage is from 0 to 3.39 V, and the temperature range is from 22 to 28 °C. The current jump is caused by the switching between charging and discharging of the energy storage power station. The SOC ranges from 17.5 to 86.6%.

What is a time series prediction method for voltage anomalies?

Informer-based time series prediction method for voltage anomalies. In the back propagation process of neural networks, the loss function plays a crucial role and essentially reflects the error of the network. The smaller the value of the loss function, the more superior the performance of the network in problem solving.

What are the parameters of voltage abnormity prediction model based on Informer?

Table 1 Parameters of voltage abnormity prediction model based on informer. BO neural networks encompass several hyperparameters, including the loss function, the number of encoder layers, the number of decoder layers, h-len, learning rate, dropout rate, and batch size.