What does this research mean for the field?

The BiLSTM-GRU model significantly improves the accuracy of renewable energy output and load demand forecasting in smart microgrids compared to traditional machine and deep learning models. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To evaluate the effectiveness of a hybrid BiLSTM-GRU model in forecasting renewable energy output and load demand in microgrids.

March 6, 2026Open Access

A comparative study of machine learning and deep learning models for renewable energy and load demand forecasting in smart microgrids

KRK. RavivarmaSRM University BLB. LokeshguptaMotilal Nehru National Institute of Technology RURamanjaneya Reddy UdumulaSRM University

Puntos clave

To evaluate the effectiveness of a hybrid BiLSTM-GRU model in forecasting renewable energy output and load demand in microgrids.
Developed a BiLSTM-GRU prediction model for forecasting RER output and load demand.
Compared performance with various models: SVM, RF, GBM, LSTM, BiLSTM, GRU, and LSTM-GRU.
Evaluated model accuracy using MSE, MAE, RMSE, and NRMSE across different time horizons: 6 h, 12 h, 18 h, and 24 h ahead.
Performed Diebold-Mariano test to assess statistical significance of the prediction improvements.
BiLSTM-GRU achieved the lowest MSE of 0.000019 for solar output power prediction.
For wind output power, MSE improved to 0.00090 using BiLSTM-GRU.
Load demand forecasting reached an MSE of 0.00022 with the BiLSTM-GRU model.
Statistically significant improvements were confirmed with average p values below 0.05 in the DM test.
Training and testing times were efficient: 49 seconds and 0.3 seconds respectively, suited for real-time applications.

Resumen

This paper presents a hybrid deep learning-based prediction model, i.e., a bidirectional long short-term memory-gated recurrent unit (BiLSTM-GRU) model to forecast the uncertain RER output powers and load demand data in the MG environment. The proposed BiLSTM-GRU model enhances the prediction accuracy by incorporating the strengths of both BiLSTM and GRU models, which capture the data with complex temporal patterns and interdependencies and reduce the complexity of the network structure. The prediction accuracy and performance efficiency of the proposed model have been compared with the different machine and deep learning models such as support vector machine (SVM), random forest regression (RF), gradient boosting machine (GBM), long-short term memory (LSTM), BiLSTM, GRU, and a hybrid LSTM-GRU with the help of various performance metrics. The performance evaluation has been tested with four distinct time horizons: 6 h, 12 h, 18 h, and 24 h ahead. The BiLSTM-GRU model outperformed when compared all other prediction models, achieving improved performance metric values, which are 0.000019 in MSE, 0.0020 in MAE, 0.0044 in RMSE, and 0.0246 in NRMSE for solar output power prediction. In the case of wind output power prediction, 0.00090 in MSE, 0.0215 in MAE, 0.0300 in RMSE, and 0.0763 in NRMSE values are obtained. Similarly, for the load demand data prediction, 0.00022 in MSE, 0.0124 in MAE, 0.0150 in RMSE, and 0.0206 in NRMSE are achieved. Furthermore, to validate the performance of the proposed model, a Diebold-Mariano (DM) test is performed. The DM results confirmed that the proposed model obtains the statistically significant improvements over benchmark models with average p values of 0.0288, 0.0410, and 0.027 (p < 0.05) for solar output power, wind output power and load demand data, respectively. The computational efficiency of the proposed model has also demonstrated, which requires only 49 s for training process and 0.3 s for testing process, and making it suitable for the real-time energy forecasting applications. The overall results analysis clearly shows the superior performance of the proposed BiLSTM-GRU model for the forecasting of RER output powers and load demand data with the highest accuracy.

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