Multistep Forecasting of Power Flow Based on LSTM Autoencoder: A Study Case in Regional Grid Cluster Proposal

Abstract

A regional grid cluster proposal is required to tackle power grid complexities and evaluate the impact of decentralized renewable energy generation. However, implementing regional grid clusters poses challenges in power flow forecasting owing to the inherent variability of renewable power generation and diverse power load behavior. Accurate forecasting is vital for monitoring the imported power during peak regional load periods and surplus power generation exported from the studied region. This study addressed the challenge of multistep bidirectional power flow forecasting by proposing an LSTM autoencoder model. During the training stage, the proposed model and baseline models were developed using autotune hyperparameters to fine-tune the models and maximize their performance. The model utilized the last 6 h leading up to the current time (24 steps of 15 min intervals) to predict the power flow 1 h ahead (4 steps of 15 min intervals) from the current time. In the model evaluation stage, the proposed model achieved the lowest RMSE and MAE scores with values of 32.243 MW and 24.154 MW, respectively. In addition, it achieved a good R2 score of 0.93. The evaluation metrics demonstrated that the LSTM autoencoder outperformed the other models for multistep forecasting task in a regional grid cluster proposal.

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“…DL models perform better when input variables are scaled to a standardized range. The Min-max normalization is a popular technique, which maps the original value of the dataset to a new range [14,40]. The mathematical formula for min-max normalization used in this study is presented in the following Equation ( 14):…”

“…To evaluate the predictive models, test data were input into the models to generate predictions. The accuracy of the predictions was measured using three error metrics [40,46]: root mean square error (RMSE), which is presented in Equation ( 17), mean absolute error (MAE), as shown in Equation ( 18), and the mean absolute percentage error (MAPE) in Equation (19). The RMSE measures the spread of prediction errors [14,46], while the MAE calculates the average magnitude of prediction errors [33,35], and the MAPE measures the average percentage difference between the predicted value and actual value [47,48].…”

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